subject:(Statistical inference Nonparametric tests )

1. Araújo, Larissa Barreto de. Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino.

Degree: 2013, Universidade Federal do Amazonas

URL: http://tede.ufam.edu.br/handle/tede/3843

► Alavancado pelo processo da globalização, a qualidade tornou-se função decisiva para a conquista de clientes e competitividade. Assim, as organizações estão evoluindo para o aperfeiçoamento… (more)

▼ Alavancado pelo processo da globalização, a qualidade tornou-se função decisiva para a conquista de clientes e competitividade. Assim, as organizações estão evoluindo para o aperfeiçoamento de seus métodos de gestão, com intuito de atender aos requisitos exigidos pelos clientes. Dentro do contexto da gestão da qualidade, um dos métodos mais difundidos é o Ciclo PDCA, que pode ser desdobrado dentro de cada processo da organização e para o sistema de processos em sua totalidade. No setor de serviços, a percepção do cliente é indispensável para alteração de diretriz de controle (melhorias). Para isto, a estatística pode ser utilizada para viabilizar a coleta, o processamento e a disposição das informações, de forma que o conhecimento gerado é utilizado, por meio do Ciclo PDCA, para atingir metas de melhoria. Neste sentido, o trabalho objetiva propor um modelo de inferência estatística sobre a qualidade do processo de compra pública direta em Instituição de Ensino. Para isso, foi analisada a rotina do setor de compras, na qual se fez o uso das ferramentas da qualidade e dos testes não paramétricos para fazer comparações entre grupos de fatores. Com isso, foi possível identificar os fatores que influenciam de forma significativa o aumento do tempo dos processos. A abordagem da pesquisa é qualitativa, principalmente na coleta e análise dos dados, e quantitativa no tratamento. Quanto aos fins, a natureza da pesquisa é exploratória e descritiva e quanto aos meios de investigação, um estudo de caso. Com base no marco teórico, foi possível elaborar um modelo de procedimentos para execução de uma experimentação que envolve o Ciclo PDCA, as ferramentas da qualidade e os testes não paramétricos para inferência estatística sobre a qualidade do processo. A partir da aplicação deste modelo, identificou-se que o fator valor alto é o mais significativo no processo de compra pública direta. Fora este fator, a natureza do processo classificada como material permanente e processos que tramitam por mais de dezesseis setores também são fatores que influenciam no aumento do tempo. Dessa forma, foi possível contribuir com a área de gestão da qualidade, visto que se delineou a respeito da inferência estatística na qualidade em processos de compras, um assunto pouco difundido no âmbito do setor público. Advisors/Committee Members: Silva, Ocileide Custodio da, CPF:78592267404, http://lattes.cnpq.br/6820579608406432.

Subjects/Keywords: Controle de Qualidade; Ciclo PDCA; Inferência estatística; Testes não paramétricos; PDCA Cycle; Statistical inference, Nonparametric tests.; ENGENHARIAS: ENGENHARIA DE PRODUÇÃO

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Araújo, L. B. d. (2013). Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino. (Masters Thesis). Universidade Federal do Amazonas. Retrieved from http://tede.ufam.edu.br/handle/tede/3843

Chicago Manual of Style (16^th Edition):

Araújo, Larissa Barreto de. “Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino.” 2013. Masters Thesis, Universidade Federal do Amazonas. Accessed January 19, 2021. http://tede.ufam.edu.br/handle/tede/3843.

MLA Handbook (7^th Edition):

Araújo, Larissa Barreto de. “Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino.” 2013. Web. 19 Jan 2021.

Vancouver:

Araújo LBd. Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino. [Internet] [Masters thesis]. Universidade Federal do Amazonas; 2013. [cited 2021 Jan 19]. Available from: http://tede.ufam.edu.br/handle/tede/3843.

Council of Science Editors:

Araújo LBd. Inferência estatística sobre a qualidade do processo de compras públicas diretas em uma Instituição de ensino. [Masters Thesis]. Universidade Federal do Amazonas; 2013. Available from: http://tede.ufam.edu.br/handle/tede/3843

2. Ara?jo, Larissa Barreto de. Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino.

Degree: 2013, Universidade Federal do Amazonas

URL: http://tede.ufam.edu.br/handle/tede/3843

► Alavancado pelo processo da globaliza??o, a qualidade tornou-se fun??o decisiva para a conquista de clientes e competitividade. Assim, as organiza??es est?o evoluindo para o aperfei?oamento… (more)

▼ Alavancado pelo processo da globaliza??o, a qualidade tornou-se fun??o decisiva para a conquista de clientes e competitividade. Assim, as organiza??es est?o evoluindo para o aperfei?oamento de seus m?todos de gest?o, com intuito de atender aos requisitos exigidos pelos clientes. Dentro do contexto da gest?o da qualidade, um dos m?todos mais difundidos ? o Ciclo PDCA, que pode ser desdobrado dentro de cada processo da organiza??o e para o sistema de processos em sua totalidade. No setor de servi?os, a percep??o do cliente ? indispens?vel para altera??o de diretriz de controle (melhorias). Para isto, a estat?stica pode ser utilizada para viabilizar a coleta, o processamento e a disposi??o das informa??es, de forma que o conhecimento gerado ? utilizado, por meio do Ciclo PDCA, para atingir metas de melhoria. Neste sentido, o trabalho objetiva propor um modelo de infer?ncia estat?stica sobre a qualidade do processo de compra p?blica direta em Institui??o de Ensino. Para isso, foi analisada a rotina do setor de compras, na qual se fez o uso das ferramentas da qualidade e dos testes n?o param?tricos para fazer compara??es entre grupos de fatores. Com isso, foi poss?vel identificar os fatores que influenciam de forma significativa o aumento do tempo dos processos. A abordagem da pesquisa ? qualitativa, principalmente na coleta e an?lise dos dados, e quantitativa no tratamento. Quanto aos fins, a natureza da pesquisa ? explorat?ria e descritiva e quanto aos meios de investiga??o, um estudo de caso. Com base no marco te?rico, foi poss?vel elaborar um modelo de procedimentos para execu??o de uma experimenta??o que envolve o Ciclo PDCA, as ferramentas da qualidade e os testes n?o param?tricos para infer?ncia estat?stica sobre a qualidade do processo. A partir da aplica??o deste modelo, identificou-se que o fator valor alto ? o mais significativo no processo de compra p?blica direta. Fora este fator, a natureza do processo classificada como material permanente e processos que tramitam por mais de dezesseis setores tamb?m s?o fatores que influenciam no aumento do tempo. Dessa forma, foi poss?vel contribuir com a ?rea de gest?o da qualidade, visto que se delineou a respeito da infer?ncia estat?stica na qualidade em processos de compras, um assunto pouco difundido no ?mbito do setor p?blico. Advisors/Committee Members: Silva, Ocileide Cust?dio da, CPF:78592267404, http://lattes.cnpq.br/6820579608406432.

Subjects/Keywords: Controle de Qualidade; Ciclo PDCA; Infer?ncia estat?stica; Testes n?o param?tricos; PDCA Cycle; Statistical inference, Nonparametric tests.; ENGENHARIAS: ENGENHARIA DE PRODU??O

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ara?jo, L. B. d. (2013). Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino. (Masters Thesis). Universidade Federal do Amazonas. Retrieved from http://tede.ufam.edu.br/handle/tede/3843

Chicago Manual of Style (16^th Edition):

Ara?jo, Larissa Barreto de. “Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino.” 2013. Masters Thesis, Universidade Federal do Amazonas. Accessed January 19, 2021. http://tede.ufam.edu.br/handle/tede/3843.

MLA Handbook (7^th Edition):

Ara?jo, Larissa Barreto de. “Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino.” 2013. Web. 19 Jan 2021.

Vancouver:

Ara?jo LBd. Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino. [Internet] [Masters thesis]. Universidade Federal do Amazonas; 2013. [cited 2021 Jan 19]. Available from: http://tede.ufam.edu.br/handle/tede/3843.

Council of Science Editors:

Ara?jo LBd. Infer?ncia estat?stica sobre a qualidade do processo de compras p?blicas diretas em uma Institui??o de ensino. [Masters Thesis]. Universidade Federal do Amazonas; 2013. Available from: http://tede.ufam.edu.br/handle/tede/3843

Western Michigan University

3. Dykes, Bradford M. Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study.

Degree: PhD, Statistics, 2016, Western Michigan University

URL: https://scholarworks.wmich.edu/dissertations/1958

► Over the past 10 years, the Department of Statistics at Western Michigan University has developed a question generating system that can be used for… (more)

▼ Over the past 10 years, the Department of Statistics at Western Michigan University has developed a question generating system that can be used for creating multiple forms of exams, quizzes and homework for online and face-to-face use. This system can also be used to provide students with a form of instantaneous feedback. With the goal of analyzing how different levels of feedback in an online learning environment impacts students' performance on assignments, this study presents data collected on two semesters of students enrolled in three different meeting types (strictly online, typical face-to-face, and honors face-to-face) of an introductory Statistics course. The study discusses appropriate methods available to analyze these complex data as well as issues related to computing. In general, this study found the highest level interaction to be significant, suggesting that students in various meeting types learn from feedback differently for assorted quizzes. Additionally, in factorial analyses, there are sometimes situations where there is an anticipated direction in which the treatment levels differ. Consider, for example, students' scores on an assessment. A researcher might anticipate that students in an honors section of the course will perform better than students in a non-honors section. Furthermore, one might expect that post-assessment scores would be higher than pre-assessment scores. There are statistical tests that can provide more powerful results than those tests that do not take this a-prior information into consideration. For a crossed factorial design, the lattice-ordered test is a method used for testing for an overall increase across all factor-levels. For this study, I extended this method to a weighted version that, for certain instances, outperforms the unweighted version. I also developed a novel method for nested factorial designs that test for an overall increase among all nested factor-levels. Within this context, I determined the exact distribution, the exact conditional distribution that adjusts for ties, and the asymptotic distribution. I found that this method has stable Type I error rates and outperforms a parametric version of the test for heavy tailed error distributions. Type I error rates and power estimates were computed via simulation studies for both crossed and nested designs. Advisors/Committee Members: Dr. Jeffrey Terpstra, Dr. Christine Browning, Dr. Joshua Naranjo.

Subjects/Keywords: Nonparametric methods; online feedback; statistical computation; restricted inference problems; statistical education study statistics; Applied Statistics; Design of Experiments and Sample Surveys

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dykes, B. M. (2016). Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study. (Doctoral Dissertation). Western Michigan University. Retrieved from https://scholarworks.wmich.edu/dissertations/1958

Chicago Manual of Style (16^th Edition):

Dykes, Bradford M. “Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study.” 2016. Doctoral Dissertation, Western Michigan University. Accessed January 19, 2021. https://scholarworks.wmich.edu/dissertations/1958.

MLA Handbook (7^th Edition):

Dykes, Bradford M. “Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study.” 2016. Web. 19 Jan 2021.

Vancouver:

Dykes BM. Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study. [Internet] [Doctoral dissertation]. Western Michigan University; 2016. [cited 2021 Jan 19]. Available from: https://scholarworks.wmich.edu/dissertations/1958.

Council of Science Editors:

Dykes BM. Some Nonparametric Ordered Restricted Inference Problems in the Context of a Statistical Education Study. [Doctoral Dissertation]. Western Michigan University; 2016. Available from: https://scholarworks.wmich.edu/dissertations/1958

University of North Texas

4. Chen, Andrew H. (Andrew Hwa-Fen). Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale.

Degree: 1994, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc278300/

► The purpose of this research was to evaluate the effect on parametric and nonparametric tests using ordinal data when the distances between points changed on… (more)

Subjects/Keywords: parametric tests; nonparametric tests; Mathematical statistics.; Statistical hypothesis testing.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, A. H. (. H. (1994). Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc278300/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Andrew H (Andrew Hwa-Fen). “Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale.” 1994. Thesis, University of North Texas. Accessed January 19, 2021. https://digital.library.unt.edu/ark:/67531/metadc278300/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Andrew H (Andrew Hwa-Fen). “Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale.” 1994. Web. 19 Jan 2021.

Vancouver:

Chen AH(H. Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale. [Internet] [Thesis]. University of North Texas; 1994. [cited 2021 Jan 19]. Available from: https://digital.library.unt.edu/ark:/67531/metadc278300/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen AH(H. Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale. [Thesis]. University of North Texas; 1994. Available from: https://digital.library.unt.edu/ark:/67531/metadc278300/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tampere University

5. Lu, Chien. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .

Degree: 2018, Tampere University

URL: https://trepo.tuni.fi/handle/10024/104066

► Entropy estimation is an important technique to summarize the uncertainty of a distribution underlying a set of samples. It ties to important research problems in… (more)

Subjects/Keywords: entropy estimation; nonparametric estimator; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lu, C. (2018). An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/104066

Chicago Manual of Style (16^th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Masters Thesis, Tampere University. Accessed January 19, 2021. https://trepo.tuni.fi/handle/10024/104066.

MLA Handbook (7^th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Web. 19 Jan 2021.

Vancouver:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2021 Jan 19]. Available from: https://trepo.tuni.fi/handle/10024/104066.

Council of Science Editors:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/104066

University of Kentucky

6. Li, Yuntong. Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups.

Degree: 2020, University of Kentucky

URL: https://uknowledge.uky.edu/statistics_etds/44

► Comparing the distribution of biomarker measurements between two groups under either an unpaired or paired design is a common goal in many biomarker studies. However,… (more)

Subjects/Keywords: Biomarker; Differential abundance analysis; Semi-parametric log-linear model; Nonparametric tests; Optimum weights; Missing data; Applied Statistics; Biostatistics; Statistical Methodology; Statistical Models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, Y. (2020). Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/statistics_etds/44

Chicago Manual of Style (16^th Edition):

Li, Yuntong. “Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups.” 2020. Doctoral Dissertation, University of Kentucky. Accessed January 19, 2021. https://uknowledge.uky.edu/statistics_etds/44.

MLA Handbook (7^th Edition):

Li, Yuntong. “Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups.” 2020. Web. 19 Jan 2021.

Vancouver:

Li Y. Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups. [Internet] [Doctoral dissertation]. University of Kentucky; 2020. [cited 2021 Jan 19]. Available from: https://uknowledge.uky.edu/statistics_etds/44.

Council of Science Editors:

Li Y. Semiparametric and Nonparametric Methods for Comparing Biomarker Levels between Groups. [Doctoral Dissertation]. University of Kentucky; 2020. Available from: https://uknowledge.uky.edu/statistics_etds/44

Leiden University

7. Linting, M. Nonparametric inference in nonlinear principal components analysis: Exploration and beyond.

Degree: 2007, Leiden University

URL: http://hdl.handle.net/1887/12386

► In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have… (more)

Subjects/Keywords: Nonlinear principal components analysis; PCA; CATPCA; Optimal scaling; Nonparametric inference; Nonparametric bootstrap; Permutation tests; Nonlinear principal components analysis; PCA; CATPCA; Optimal scaling; Nonparametric inference; Nonparametric bootstrap; Permutation tests

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Linting, M. (2007). Nonparametric inference in nonlinear principal components analysis: Exploration and beyond. (Doctoral Dissertation). Leiden University. Retrieved from http://hdl.handle.net/1887/12386

Chicago Manual of Style (16^th Edition):

Linting, M. “Nonparametric inference in nonlinear principal components analysis: Exploration and beyond.” 2007. Doctoral Dissertation, Leiden University. Accessed January 19, 2021. http://hdl.handle.net/1887/12386.

MLA Handbook (7^th Edition):

Linting, M. “Nonparametric inference in nonlinear principal components analysis: Exploration and beyond.” 2007. Web. 19 Jan 2021.

Vancouver:

Linting M. Nonparametric inference in nonlinear principal components analysis: Exploration and beyond. [Internet] [Doctoral dissertation]. Leiden University; 2007. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1887/12386.

Council of Science Editors:

Linting M. Nonparametric inference in nonlinear principal components analysis: Exploration and beyond. [Doctoral Dissertation]. Leiden University; 2007. Available from: http://hdl.handle.net/1887/12386

Carnegie Mellon University

8. Liu, Han. Nonparametric Learning in High Dimensions.

Degree: 2010, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/16

► This thesis develops flexible and principled nonparametric learning algorithms to explore, understand, and predict high dimensional and complex datasets. Such data appear frequently in modern… (more)

Subjects/Keywords: machine learning; statistical inference; nonparametric methods; curse of dimensionality; regression; classification; multi-task learning; density estimation; undirected graphical models; structure learning; spatial-temporal adaptive learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, H. (2010). Nonparametric Learning in High Dimensions. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/16

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Han. “Nonparametric Learning in High Dimensions.” 2010. Thesis, Carnegie Mellon University. Accessed January 19, 2021. http://repository.cmu.edu/dissertations/16.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Han. “Nonparametric Learning in High Dimensions.” 2010. Web. 19 Jan 2021.

Vancouver:

Liu H. Nonparametric Learning in High Dimensions. [Internet] [Thesis]. Carnegie Mellon University; 2010. [cited 2021 Jan 19]. Available from: http://repository.cmu.edu/dissertations/16.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu H. Nonparametric Learning in High Dimensions. [Thesis]. Carnegie Mellon University; 2010. Available from: http://repository.cmu.edu/dissertations/16

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université Paris-Sud – Paris XI

9. Dumont, Thierry. Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators.

Degree: Docteur es, Mathématiques, 2012, Université Paris-Sud – Paris XI

URL: http://www.theses.fr/2012PA112379

►

Préfigurant la prochaine grande étape dans le domaine de la navigation, la géolocalisation intra-muros est un domaine de recherche très actif depuis quelques années. Alors… (more)

▼

Préfigurant la prochaine grande étape dans le domaine de la navigation, la géolocalisation intra-muros est un domaine de recherche très actif depuis quelques années. Alors que la géolocalisation est entrée dans le quotidien de nombreux professionnels et particuliers avec, notamment, le guidage routier assisté, les besoins d'étendre les applications à l'intérieur se font de plus en plus pressants. Cependant, les systèmes existants se heurtent à des contraintes techniques bien supérieures à celles rencontrées à l'extérieur, la faute, notamment, à la propagation chaotique des ondes électromagnétiques dans les environnements confinés et inhomogènes. Nous proposons dans ce manuscrit une approche statistique du problème de géolocalisation d'un mobile à l'intérieur d'un bâtiment utilisant les ondes WiFi environnantes. Ce manuscrit s'articule autour de deux questions centrales : celle de la détermination des cartes de propagation des ondes WiFi dans un bâtiment donné et celle de la construction d'estimateurs des positions du mobile à l'aide de ces cartes de propagation. Le cadre statistique utilisé dans cette thèse afin de répondre à ces questions est celui des modèles de Markov cachés. Nous proposons notamment, dans un cadre paramétrique, une méthode d'inférence permettant l'estimation en ligne des cartes de propagation, sur la base des informations relevées par le mobile. Dans un cadre non-paramétrique, nous avons étudié la possibilité d'estimer les cartes de propagation considérées comme simple fonction régulière sur l'environnement à géolocaliser. Nos résultats sur l'estimation non paramétrique dans les modèles de Markov cachés permettent d'exhiber un estimateur des fonctions de propagation dont la consistance est établie dans un cadre général. La dernière partie du manuscrit porte sur l'estimation de l'arbre de contextes dans les modèles de Markov cachés à longueur variable.

Foreshadowing the next big step in the field of navigation, indoor geolocation has been a very active field of research in the last few years. While geolocation entered the life of many individuals and professionals, particularly through assisted navigation systems on roads, needs to extend the applications inside the buildings are more and more present. However, existing systems face many more technical constraints than those encountered outside, including the chaotic propagation of electromagnetic waves in confined and inhomogeneous environments. In this manuscript, we propose a statistical approach to the problem of geolocation of a mobile device inside a building, using the WiFi surrounding waves. This manuscript focuses on two central issues: the determination of WiFi wave propagation maps inside a building and the construction of estimators of the mobile's positions using these propagation maps. The statistical framework used in this thesis to answer these questions is that of hidden Markov models. We propose, in a parametric framework, an inference method for the online estimation of the propagation maps, on the basis of the informations…

Advisors/Committee Members: Gassiat, Elisabeth (thesis director).

Subjects/Keywords: Localisation intra-muros; WiFi; Modèles de Markov cachés; Inférence statistique; Estimation non-paramétrique; Estimation en ligne; Indoor localization; WiFi; Hidden Markov models; Statistical inference; Nonparametric estimation; Online estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dumont, T. (2012). Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2012PA112379

Chicago Manual of Style (16^th Edition):

Dumont, Thierry. “Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators.” 2012. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed January 19, 2021. http://www.theses.fr/2012PA112379.

MLA Handbook (7^th Edition):

Dumont, Thierry. “Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators.” 2012. Web. 19 Jan 2021.

Vancouver:

Dumont T. Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2012. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2012PA112379.

Council of Science Editors:

Dumont T. Contributions à la localisation intra-muros. De la modélisation à la calibration théorique et pratique d'estimateurs : Contributions to the indoor localisation. From the modelization to the theoretical and practical calibration of estimators. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2012. Available from: http://www.theses.fr/2012PA112379

Kansas State University

10. Von Borries, George Freitas. Partition clustering of High Dimensional Low Sample Size data based on P-Values.

Degree: PhD, Department of Statistics, 2008, Kansas State University

URL: http://hdl.handle.net/2097/590

► This thesis introduces a new partitioning algorithm to cluster variables in high dimensional low sample size (HDLSS) data and high dimensional longitudinal low sample size… (more)

▼ This thesis introduces a new partitioning algorithm to cluster variables in high dimensional low sample size (HDLSS) data and high dimensional longitudinal low sample size (HDLLSS) data. HDLSS data contain a large number of variables with small number of replications per variable, and HDLLSS data refer to HDLSS data observed over time. Clustering technique plays an important role in analyzing high dimensional low sample size data as is seen commonly in microarray experiment, mass spectrometry data, pattern recognition. Most current clustering algorithms for HDLSS and HDLLSS data are adaptations from traditional multivariate analysis, where the number of variables is not high and sample sizes are relatively large. Current algorithms show poor performance when applied to high dimensional data, especially in small sample size cases. In addition, available algorithms often exhibit poor clustering accuracy and stability for non-normal data. Simulations show that traditional clustering algorithms used in high dimensional data are not robust to monotone transformations. The proposed clustering algorithm PPCLUST is a powerful tool for clustering HDLSS data, which uses p-values from nonparametric rank tests of homogeneous distribution as a measure of similarity between groups of variables. Inherited from the robustness of rank procedure, the new algorithm is robust to outliers and invariant to monotone transformations of data. PPCLUSTEL is an extension of PPCLUST for clustering of HDLLSS data. A nonparametric test of no simple effect of group is developed and the p-value from the test is used as a measure of similarity between groups of variables. PPCLUST and PPCLUSTEL are able to cluster a large number of variables in the presence of very few replications and in case of PPCLUSTEL, the algorithm require neither a large number nor equally spaced time points. PPCLUST and PPCLUSTEL do not suffer from loss of power due to distributional assumptions, general multiple comparison problems and difficulty in controlling heterocedastic variances. Applications with available data from previous microarray studies show promising results and simulations studies reveal that the algorithm outperforms a series of benchmark algorithms applied to HDLSS data exhibiting high clustering accuracy and stability. Advisors/Committee Members: Haiyan Wang.

Subjects/Keywords: High Dimensional Data; Clustering; Nonparametric Inference; Bioinformatics; Statistical Learning; Data Mining; Statistics (0463)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Von Borries, G. F. (2008). Partition clustering of High Dimensional Low Sample Size data based on P-Values. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/590

Chicago Manual of Style (16^th Edition):

Von Borries, George Freitas. “Partition clustering of High Dimensional Low Sample Size data based on P-Values.” 2008. Doctoral Dissertation, Kansas State University. Accessed January 19, 2021. http://hdl.handle.net/2097/590.

MLA Handbook (7^th Edition):

Von Borries, George Freitas. “Partition clustering of High Dimensional Low Sample Size data based on P-Values.” 2008. Web. 19 Jan 2021.

Vancouver:

Von Borries GF. Partition clustering of High Dimensional Low Sample Size data based on P-Values. [Internet] [Doctoral dissertation]. Kansas State University; 2008. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2097/590.

Council of Science Editors:

Von Borries GF. Partition clustering of High Dimensional Low Sample Size data based on P-Values. [Doctoral Dissertation]. Kansas State University; 2008. Available from: http://hdl.handle.net/2097/590

University of Canterbury

11. Harlow, Jennifer. Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.

Degree: MS, Statistics, 2013, University of Canterbury

URL: http://dx.doi.org/10.26021/1899

► A regular paving (RP) is a finite succession of bisections that partitions a multidimensional box into sub-boxes using a binary tree-based data structure, with the… (more)

Subjects/Keywords: statistical regular paving; real-mapped regular paving; data-adaptive histogram; multivariate histogram; nonparametric density estimation; Markov Chain Monte Carlo; regular paving approximate Bayesian computation; simulation-intensive inference

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APA (6^th Edition):

Harlow, J. (2013). Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. (Masters Thesis). University of Canterbury. Retrieved from http://dx.doi.org/10.26021/1899

Chicago Manual of Style (16^th Edition):

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Masters Thesis, University of Canterbury. Accessed January 19, 2021. http://dx.doi.org/10.26021/1899.

MLA Handbook (7^th Edition):

Harlow, Jennifer. “Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference.” 2013. Web. 19 Jan 2021.

Vancouver:

Harlow J. Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. [Internet] [Masters thesis]. University of Canterbury; 2013. [cited 2021 Jan 19]. Available from: http://dx.doi.org/10.26021/1899.

Council of Science Editors:

Harlow J. Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference. [Masters Thesis]. University of Canterbury; 2013. Available from: http://dx.doi.org/10.26021/1899

12. Brécheteau, Claire. Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference.

Degree: Docteur es, Mathématiques appliquées, 2018, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2018SACLS334

►

Le volume de données disponibles est en perpétuelle expansion. Il est primordial de fournir des méthodes efficaces et robustes permettant d'en extraire des informations pertinentes.… (more)

▼

Le volume de données disponibles est en perpétuelle expansion. Il est primordial de fournir des méthodes efficaces et robustes permettant d'en extraire des informations pertinentes. Nous nous focalisons sur des données pouvant être représentées sous la forme de nuages de points dans un certain espace muni d'une métrique, e.g. l'espace Euclidien R^d, générées selon une certaine distribution. Parmi les questions naturelles que l'on peut se poser lorsque l'on a accès à des données, trois d'entre elles sont abordées dans cette thèse. La première concerne la comparaison de deux ensembles de points. Comment décider si deux nuages de points sont issus de formes ou de distributions similaires ? Nous construisons un test statistique permettant de décider si deux nuages de points sont issus de distributions égales (modulo un certain type de transformations e.g. symétries, translations, rotations...). La seconde question concerne la décomposition d'un ensemble de points en plusieurs groupes. Étant donné un nuage de points, comment faire des groupes pertinents ? Souvent, cela consiste à choisir un système de k représentants et à associer chaque point au représentant qui lui est le plus proche, en un sens à définir. Nous développons des méthodes adaptées à des données échantillonnées selon certains mélanges de k distributions, en présence de données aberrantes. Enfin, lorsque les données n'ont pas naturellement une structure en k groupes, par exemple, lorsqu'elles sont échantillonnées à proximité d'une sous-variété de R^d, une question plus pertinente est de construire un système de k représentants, avec k grand, à partir duquel on puisse retrouver la sous-variété. Cette troisième question recouvre le problème de la quantification d'une part, et le problème de l'approximation de la distance à un ensemble d'autre part. Pour ce faire, nous introduisons et étudions une variante de la méthode des k-moyennes adaptée à la présence de données aberrantes dans le contexte de la quantification. Les réponses que nous apportons à ces trois questions dans cette thèse sont de deux types, théoriques et algorithmiques. Les méthodes proposées reposent sur des objets continus construits à partir de distributions et de sous-mesures. Des études statistiques permettent de mesurer la proximité entre les objets empiriques et les objets continus correspondants. Ces méthodes sont faciles à implémenter en pratique lorsque des nuages de points sont à disposition. L'outil principal utilisé dans cette thèse est la fonction distance à la mesure, introduite à l'origine pour adapter les méthodes d'analyse topologique des données à des nuages de points corrompus par des données aberrantes

It is primordial to establish effective and robust methods to extract pertinent information from datasets. We focus on datasets that can be represented as point clouds in some metric space, e.g. Euclidean space R^d; and that are generated according to some distribution. Of the natural questions that may arise when one has access to data, three are addressed in this thesis.…

Advisors/Committee Members: Massart, Pascal (thesis director), Chazal, Frédéric (thesis director).

Subjects/Keywords: Analyse géométrique des données; Distance à la mesure; Tests statistiques; Partitionnement; Quantification; Inférence de support; Geometric data analysis; Distance-To-Measure; Statistical tests; Clustering; Quantization; Support inference

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APA (6^th Edition):

Brécheteau, C. (2018). Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2018SACLS334

Chicago Manual of Style (16^th Edition):

Brécheteau, Claire. “Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference.” 2018. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed January 19, 2021. http://www.theses.fr/2018SACLS334.

MLA Handbook (7^th Edition):

Brécheteau, Claire. “Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference.” 2018. Web. 19 Jan 2021.

Vancouver:

Brécheteau C. Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2018. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2018SACLS334.

Council of Science Editors:

Brécheteau C. Vers une vision robuste de l'inférence géométrique : Toward a Robust Vision of Geometrical Inference. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2018. Available from: http://www.theses.fr/2018SACLS334

Columbia University

13. Resa Juárez, María de los Angeles. Essays on Matching and Weighting for Causal Inference in Observational Studies.

Degree: 2017, Columbia University

URL: https://doi.org/10.7916/D8959W4H

► This thesis consists of three papers on matching and weighting methods for causal inference. The first paper conducts a Monte Carlo simulation study to evaluate… (more)

▼ This thesis consists of three papers on matching and weighting methods for causal inference. The first paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers, and the more recently proposed methods, optimal matching of an optimally chosen subset and optimal cardinality matching. The main findings are: (i) covariate balance, as measured by differences in means, variance ratios, Kolmogorov-Smirnov distances, and cross-match test statistics, is better with cardinality matching since by construction it satisfies balance requirements; (ii) for given levels of covariate balance, the matched samples are larger with cardinality matching than with the other methods; (iii) in terms of covariate distances, optimal subset matching performs best; (iv) treatment effect estimates from cardinality matching have lower RMSEs, provided strong requirements for balance, specifically, fine balance, or strength-k balance, plus close mean balance. In standard practice, a matched sample is considered to be balanced if the absolute differences in means of the covariates across treatment groups are smaller than 0.1 standard deviations. However, the simulation results suggest that stronger forms of balance should be pursued in order to remove systematic biases due to observed covariates when a difference in means treatment effect estimator is used. In particular, if the true outcome model is additive then marginal distributions should be balanced, and if the true outcome model is additive with interactions then low-dimensional joints should be balanced. The second paper focuses on longitudinal studies, where marginal structural models (MSMs) are widely used to estimate the effect of time-dependent treatments in the presence of time-dependent confounders. Under a sequential ignorability assumption, MSMs yield unbiased treatment effect estimates by weighting each observation by the inverse of the probability of their observed treatment sequence given their history of observed covariates. However, these probabilities are typically estimated by fitting a propensity score model, and the resulting weights can fail to adjust for observed covariates due to model misspecification. Also, these weights tend to yield very unstable estimates if the predicted probabilities of treatment are very close to zero, which is often the case in practice. To address both of these problems, instead of modeling the probabilities of treatment, a design-based approach is taken and weights of minimum variance that adjust for the covariates across all possible treatment histories are directly found. For this, the role of weighting in longitudinal studies of treatment effects is analyzed, and a convex optimization problem that can be solved efficiently is defined. Unlike standard methods, this approach makes evident to the investigator the limitations…

Subjects/Keywords: Statistics; Inference; Statistical matching; Probabilities

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APA (6^th Edition):

Resa Juárez, M. d. l. A. (2017). Essays on Matching and Weighting for Causal Inference in Observational Studies. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D8959W4H

Chicago Manual of Style (16^th Edition):

Resa Juárez, María de los Angeles. “Essays on Matching and Weighting for Causal Inference in Observational Studies.” 2017. Doctoral Dissertation, Columbia University. Accessed January 19, 2021. https://doi.org/10.7916/D8959W4H.

MLA Handbook (7^th Edition):

Resa Juárez, María de los Angeles. “Essays on Matching and Weighting for Causal Inference in Observational Studies.” 2017. Web. 19 Jan 2021.

Vancouver:

Resa Juárez MdlA. Essays on Matching and Weighting for Causal Inference in Observational Studies. [Internet] [Doctoral dissertation]. Columbia University; 2017. [cited 2021 Jan 19]. Available from: https://doi.org/10.7916/D8959W4H.

Council of Science Editors:

Resa Juárez MdlA. Essays on Matching and Weighting for Causal Inference in Observational Studies. [Doctoral Dissertation]. Columbia University; 2017. Available from: https://doi.org/10.7916/D8959W4H

Princeton University

14. Zhao, Tianqi. Statistical Inference for Big Data .

Degree: PhD, 2017, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp017d278w62w

► This dissertation develops novel inferential methods and theory for assessing uncertainty of modern statistical procedures unique to big data analysis. In particular, we mainly focus… (more)

▼ This dissertation develops novel inferential methods and theory for assessing uncertainty of modern statistical procedures unique to big data analysis. In particular, we mainly focus on four challenging aspects of big data: massive sample size, high dimensionality, heterogeneity and complexity. To begin with, we consider a partially linear framework for modeling massive heterogeneous data. The major goal is to extract common features across all sub-populations while exploring heterogeneity of each sub-population. In particular, we propose an aggregation type estimator for the commonality parameter that possesses the (non-asymptotic) minimax optimal bound and asymptotic distribution as if there were no heterogeneity. This oracle result holds when the number of sub-populations does not grow too fast. The next problem focuses on the challenge of the high dimensionality. We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high dimensional linear models, where the dimension p can grow exponentially fast with the sample size n. We develop a new de-biasing framework tailored for nonsmooth loss functions. Our framework enables us to exploit the composite quantile function to construct a de-biased CQR estimator. This estimator is robust, and preserves efficiency in the sense that the worst case efficiency loss is less than 30% compared to square-loss-based procedures. In many cases our estimator is close to or better than the latter. Next, we consider the problem of high dimensional semiparametric generalized linear models. We propose a new inferential framework which addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneity. First, we develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function. Then we propose a new likelihood ratio based framework to construct post-regularization confidence regions and tests for the low dimensional components of high dimensional parameters. We demonstrate the consequences of the general theory by using examples of missing data and multiple datasets inference. Lastly, we study the rank likelihood as a powerful inferential tool in multivariate analysis. The computation of the full rank likelihood function is often intractable in large-scale datasets. Motivated by this, we resort to lower order rank approximations and propose a new family of local rank likelihood functions. In particular, we show that the maximizer of the second-order local rank likelihood coincides with the Kendall's tau correlation matrix for the… Advisors/Committee Members: Liu, Han (advisor).

Subjects/Keywords: Big Data; Statistical Inference

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APA (6^th Edition):

Zhao, T. (2017). Statistical Inference for Big Data . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp017d278w62w

Chicago Manual of Style (16^th Edition):

Zhao, Tianqi. “Statistical Inference for Big Data .” 2017. Doctoral Dissertation, Princeton University. Accessed January 19, 2021. http://arks.princeton.edu/ark:/88435/dsp017d278w62w.

MLA Handbook (7^th Edition):

Zhao, Tianqi. “Statistical Inference for Big Data .” 2017. Web. 19 Jan 2021.

Vancouver:

Zhao T. Statistical Inference for Big Data . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2021 Jan 19]. Available from: http://arks.princeton.edu/ark:/88435/dsp017d278w62w.

Council of Science Editors:

Zhao T. Statistical Inference for Big Data . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp017d278w62w

University of Illinois – Urbana-Champaign

15. Xu, Jiaming. Statistical inference in networks: fundamental limits and efficient algorithms.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/72799

► Today witnesses an explosion of data coming from various types of networks such as online social networks and biological networks. The goal of this thesis… (more)

▼ Today witnesses an explosion of data coming from various types of networks such as online social networks and biological networks. The goal of this thesis is to understand when and how we can efficiently extract useful information from such network data. In the first part, we are interested in finding tight-knit communities within a network. Assuming the network is generated according to a planted cluster model, we derive a computationally efficient semidefinite programming relaxation of the maximum likelihood estimation method and obtain a stronger performance guarantee than previously known. If the community sizes are linear in the total number of vertices, the guarantee matches up to a constant factor with the information limit which we also identify, and exactly matches without a constant gap when there is a single community or two equal-sized communities. However, if the community sizes are sublinear in the total number of vertices, the guarantee is far from the information limit. We conjecture that our algorithm achieves the computational limit below which no polynomial-time algorithm can succeed. To provide evidence, we show that finding a community in some regime below the conjectured computational limit but above the information limit is computationally intractable, assuming hardness of the well-known planted clique problem. The second part studies the problem of inferring the group preference for a set of items based on the partial rankings over different subsets of the items provided by a group of users. A question of particular interest is how to optimally construct the graph used for assigning items to users for ranking. Assuming the partial rankings are generated independently according to the Plackett-Luce model, we analyze computationally efficient estimators based on maximum likelihood and rank-breaking schemes that decompose partial rankings into pairwise comparisons. We provide upper and lower bounds on the estimation error. The lower bound depends on the degree sequence of the assignment graph, while the upper bound depends on the spectral gap of the assignment graph. When the graph is an expander, the lower and upper bounds match up to a logarithmic factor. The unifying theme for the two parts of the thesis is the spectral gap of the graph. In both cases, when the graph has a large spectral gap, accurate and efficient inference is possible via maximum likelihood estimation or its convex relaxation. However, when the spectral gap vanishes, accurate inference may be statistically impossible, or it is statistically possible but may be computationally intractable. Advisors/Committee Members: Hajek, Bruce (advisor), Hajek, Bruce (Committee Chair), Srikant, R. (committee member), Oh, Sewoong (committee member), Sanghavi, Sujay (committee member), Lelarge, Marc (committee member).

Subjects/Keywords: Community detection; Networks; Statistical inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xu, J. (2015). Statistical inference in networks: fundamental limits and efficient algorithms. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72799

Chicago Manual of Style (16^th Edition):

Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 19, 2021. http://hdl.handle.net/2142/72799.

MLA Handbook (7^th Edition):

Xu, Jiaming. “Statistical inference in networks: fundamental limits and efficient algorithms.” 2015. Web. 19 Jan 2021.

Vancouver:

Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2142/72799.

Council of Science Editors:

Xu J. Statistical inference in networks: fundamental limits and efficient algorithms. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72799

University of New Orleans

16. Nguyen, Trang. Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking.

Degree: MS, Electrical Engineering, 2003, University of New Orleans

URL: https://scholarworks.uno.edu/td/20

► Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been… (more)

Subjects/Keywords: statistical inference

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APA (6^th Edition):

Nguyen, T. (2003). Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking. (Thesis). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/20

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nguyen, Trang. “Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking.” 2003. Thesis, University of New Orleans. Accessed January 19, 2021. https://scholarworks.uno.edu/td/20.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nguyen, Trang. “Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking.” 2003. Web. 19 Jan 2021.

Vancouver:

Nguyen T. Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking. [Internet] [Thesis]. University of New Orleans; 2003. [cited 2021 Jan 19]. Available from: https://scholarworks.uno.edu/td/20.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nguyen T. Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking. [Thesis]. University of New Orleans; 2003. Available from: https://scholarworks.uno.edu/td/20

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales

17. Stindl, Tom. Statistical inference for renewal Hawkes self-exciting point processes.

Degree: Mathematics & Statistics, 2019, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/64899 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:62891/SOURCE02?view=true

► The class of self-exciting point process evolve within a self-excitation mechanism that allows past events to contribute to the arrival rate of future events. The… (more)

▼ The class of self-exciting point process evolve within a self-excitation mechanism that allows past events to contribute to the arrival rate of future events. The significant contributions this thesis introduces are techniques to conduct efficient statistical inferences for the recently proposed renewal Hawkes self-exciting point processes. By employing a substantial modification to the baseline arrival rate of the Hawkes process, the renewal Hawkes process provides superior versatility. The additional flexibility afforded to the renewal Hawkes process occurs by defining the immigration process in terms of a general renewal process rather than a homogenous Poisson process. The renewal Hawkes process has the potential to widen the application domains of self-exciting processes significantly. However, it was initially asserted that likelihood evaluation of the process demands exponential computational time and therefore is practically impossible. As a consequence, two Expectation-Maximization (E-M) algorithms were developed to compute the maximum likelihood estimator (MLE), a bootstrap procedure to estimate the variance-covariance matrix of the MLE and a Monte Carlo approach to compute a goodness-of-fit test statistic.Considering the fundamental role played by the likelihood function in statistical inferences, a practically feasible method for likelihood evaluation is highly desirable. This thesis develops algorithms to evaluate the likelihood of the renewal Hawkes process in quadratic time, a drastic improvement from the exponential time initially claimed. Simulations will demonstrate the superior performance of the resulting MLEs of the model relative to the E-M estimators. This thesis will also introduce computationally efficient procedures to calculate the Rosenblatt residuals of the process for goodness-of-fit assessment and a simple yet efficient procedure for future event predictions. Faster fitting methods, and linear time algorithms to fit the process are also discussed. The computational efficiency of the methods developed facilitates the application of these algorithms to multi-event and marked point process models with renewal immigration. As such, this thesis proposes two additional models termed the multivariate renewal Hawkes process and the mark renewal Hawkes process. The additional computational challenges that arise in these frameworks are solved herein. Advisors/Committee Members: Chen, Feng, Mathematics & Statistics, Faculty of Science, UNSW, Dunsmuir, William, Mathematics & Statistics, Faculty of Science, UNSW.

Subjects/Keywords: Renewal Hawkes process; Statistical inference

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APA (6^th Edition):

Stindl, T. (2019). Statistical inference for renewal Hawkes self-exciting point processes. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/64899 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:62891/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Stindl, Tom. “Statistical inference for renewal Hawkes self-exciting point processes.” 2019. Doctoral Dissertation, University of New South Wales. Accessed January 19, 2021. http://handle.unsw.edu.au/1959.4/64899 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:62891/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Stindl, Tom. “Statistical inference for renewal Hawkes self-exciting point processes.” 2019. Web. 19 Jan 2021.

Vancouver:

Stindl T. Statistical inference for renewal Hawkes self-exciting point processes. [Internet] [Doctoral dissertation]. University of New South Wales; 2019. [cited 2021 Jan 19]. Available from: http://handle.unsw.edu.au/1959.4/64899 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:62891/SOURCE02?view=true.

Council of Science Editors:

Stindl T. Statistical inference for renewal Hawkes self-exciting point processes. [Doctoral Dissertation]. University of New South Wales; 2019. Available from: http://handle.unsw.edu.au/1959.4/64899 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:62891/SOURCE02?view=true

Colorado State University

18. Liu, Teng. Nonparametric tests for informative selection and small area estimation for reconciling survey estimates.

Degree: PhD, Statistics, 2020, Colorado State University

URL: http://hdl.handle.net/10217/211817

► Two topics in the analysis of complex survey data are addressed: testing for informative selection and addressing temporal discontinuities due to survey redesign. Informative selection,… (more)

▼ Two topics in the analysis of complex survey data are addressed: testing for informative selection and addressing temporal discontinuities due to survey redesign. Informative selection, in which the distribution of response variables given that they are sampled is different from their distribution in the population, is pervasive in modern complex surveys. Failing to take such informativeness into account could produce severe inferential errors, such as biased parameter estimators, wrong coverage rates of confidence intervals, incorrect test statistics, and erroneous conclusions. While several parametric procedures exist to test for informative selection in the survey design, it is often hard to check the parametric assumptions on which those procedures are based. We propose two classes of nonparametric tests for informative selection, each motivated by a nonparametric test for two independent samples. The first nonparametric class generalizes classic two-sample tests that compare empirical cumulative distribution functions, including Kolmogorov–Smirnov and Cramér–von Mises, by comparing weighted and unweighted empirical cumulative distribution functions. The second nonparametric class adapts two-sample tests that compare distributions based on the maximum mean discrepancy to the setting of weighted and unweighted distributions. The asymptotic distributions of both test statistics are established under the null hypothesis of noninformative selection. Simulation results demonstrate the usefulness of the asymptotic approximations, and show that our tests have competitive power with parametric tests in a correctly specified parametric setting while achieving greater power in misspecified scenarios. Many surveys face the problem of comparing estimates obtained with different methodology, including differences in frames, measurement instruments, and modes of delivery. Differences may exist within the same survey; for example, multi-mode surveys are increasingly common. Further, it is inevitable that surveys need to be redesigned from time to time. Major redesign of survey processes could affect survey estimates systematically, and it is important to quantify and adjust for such discontinuities between the designs to ensure comparability of estimates over time. We propose a small area estimation approach to reconcile two sets of survey estimates, and apply it to two surveys in the Marine Recreational Information Program (MRIP). We develop a log-normal model for the estimates from the two surveys, accounting for temporal dynamics through regression on population size and state-by-wave seasonal factors, and accounting in part for changing coverage properties through regression on wireless telephone penetration. Using the estimated design variances, we develop a regression model that is analytically consistent with the log-normal mean model. We use the modeled design variances in a Fay-Herriot small area estimation procedure to obtain empirical best linear unbiased predictors of the reconciled effort estimates for all states… Advisors/Committee Members: Breidt, F. Jay (advisor), Wang, Haonan (committee member), Estep, Donald J. (committee member), Doherty, Paul F., Jr. (committee member).

Subjects/Keywords: nonparametric tests; informative selection; small area estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, T. (2020). Nonparametric tests for informative selection and small area estimation for reconciling survey estimates. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/211817

Chicago Manual of Style (16^th Edition):

Liu, Teng. “Nonparametric tests for informative selection and small area estimation for reconciling survey estimates.” 2020. Doctoral Dissertation, Colorado State University. Accessed January 19, 2021. http://hdl.handle.net/10217/211817.

MLA Handbook (7^th Edition):

Liu, Teng. “Nonparametric tests for informative selection and small area estimation for reconciling survey estimates.” 2020. Web. 19 Jan 2021.

Vancouver:

Liu T. Nonparametric tests for informative selection and small area estimation for reconciling survey estimates. [Internet] [Doctoral dissertation]. Colorado State University; 2020. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10217/211817.

Council of Science Editors:

Liu T. Nonparametric tests for informative selection and small area estimation for reconciling survey estimates. [Doctoral Dissertation]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/211817

University of Waterloo

19. Tian, Zhaoyang. Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information.

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/15867

► In this thesis, we focus on two important statistical problems. The first is the nonparametric estimation in a compound mixture model with application to the… (more)

▼ In this thesis, we focus on two important statistical problems. The first is the nonparametric estimation in a compound mixture model with application to the malaria study. The second is the control of the false discovery rate in multiple hypothesis testing applications with auxiliary information. Malaria can be diagnosed by the presence of parasites and symptoms (usually fever) due to parasites. In endemic areas, however, an individual may have fever attributable either to malaria or to other causes. Thus, the parasite level of an individual with fever follows a two-component mixture distribution, with the two components corresponding to malaria and nonmalaria individuals. Furthermore, the parasite levels of nonmalaria individuals can be characterized as a mixture of a zero component and a positive distribution, while the parasite levels of malaria individuals can only be positive. Therefore, the parasite level of an individual with fever follows a compound mixture model. In Chapter 2, we propose a maximum multinomial likelihood approach for estimating the unknown parameters/functions using parasite-level data from two groups of individuals: the first group only contains the malaria individuals, while the second group is a mixture of malaria and nonmalaria individuals. We develop an EM-algorithm to numerically calculate the maximum multinomial likelihood estimates and further establish their convergence rates. Simulation results show that the proposed maximum multinomial likelihood estimators are more efficient than existing nonparametric estimators. The proposed method is used to analyze a malaria survey data. In many multiple hypothesis testing applications, thousands of null hypotheses are tested simultaneously. For each null hypothesis, usually a test statistic and the corresponding p-value are calculated. Traditional rejection rules work on p-values and hence ignore the signs of the test statistics in two-sided tests. However, the signs may carry useful directional information in two-group comparison settings. In Chapter 3, we introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the false discovery rate in finite samples. We demonstrate the power advantage of our procedure through simulation studies and two real applications. In Chapter 4, we further extend the signed-knockoff procedure to incorporate additional information from covariates, which are subject to missing. We propose a new procedure, the covariate and direction adaptive knockoff procedure, and show that our procedure can control the false discovery rate in finite samples. Simulation studies and real data analysis show that our procedure is competitive to existing covariate-adaptive methods. In Chapter 5, we summarize our contributions and outline several interesting topics worthy of further exploration in the future.

Subjects/Keywords: Nonparametric statistics; Malaria; Testing; Statistical methods

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APA (6^th Edition):

Tian, Z. (2020). Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15867

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tian, Zhaoyang. “Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information.” 2020. Thesis, University of Waterloo. Accessed January 19, 2021. http://hdl.handle.net/10012/15867.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tian, Zhaoyang. “Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information.” 2020. Web. 19 Jan 2021.

Vancouver:

Tian Z. Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10012/15867.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tian Z. Nonparametric Estimation in a Compound Mixture Model and False Discovery Rate Control with Auxiliary Information. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15867

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Colorado State University

20. Sonderegger, Derek Lee. Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications.

Degree: PhD, Statistics, 2010, Colorado State University

URL: http://hdl.handle.net/10217/39050

► Nonparametric function estimation has proven to be a useful tool for applied statisticians. Classic techniques such as locally weighted regression and smoothing splines are being… (more)

Subjects/Keywords: spline; fiducial inference; smoothing; free-knot; Nonparametric statistics; Splines; Ecology – Research – Statistical methods; Bayesian statistical decision theory

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APA (6^th Edition):

Sonderegger, D. L. (2010). Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/39050

Chicago Manual of Style (16^th Edition):

Sonderegger, Derek Lee. “Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications.” 2010. Doctoral Dissertation, Colorado State University. Accessed January 19, 2021. http://hdl.handle.net/10217/39050.

MLA Handbook (7^th Edition):

Sonderegger, Derek Lee. “Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications.” 2010. Web. 19 Jan 2021.

Vancouver:

Sonderegger DL. Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10217/39050.

Council of Science Editors:

Sonderegger DL. Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/39050

Penn State University

21. Xiong, Sihan. BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14985sux101

► This thesis proposes a Bayesian nonparametric model of categorical data, which can be used for casual inference of time series, information fusion of heterogeneous sources… (more)

Subjects/Keywords: Bayesian nonparametric; Causal inference; Panel Data; Variational Inference; Sequential Classification; Information fusion

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APA (6^th Edition):

Xiong, S. (2018). BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14985sux101

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xiong, Sihan. “BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS.” 2018. Thesis, Penn State University. Accessed January 19, 2021. https://submit-etda.libraries.psu.edu/catalog/14985sux101.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xiong, Sihan. “BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS.” 2018. Web. 19 Jan 2021.

Vancouver:

Xiong S. BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 19]. Available from: https://submit-etda.libraries.psu.edu/catalog/14985sux101.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xiong S. BAYESIAN NONPARAMETRIC MODELING OF CATEGORICAL DATA WITH APPLICATIONS TO DYNAMIC DATA-DRIVEN SYSTEMS. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14985sux101

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

22. Linero, Antonio Richardo. Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection.

Degree: PhD, Statistics, 2015, University of Florida

URL: https://ufdc.ufl.edu/UFE0049143

► This dissertation concerns two essentially independent topics, with the primary link between the two being the use of Bayesian nonparametrics as an inference tool. The… (more)

Subjects/Keywords: Inference; Markov chains; Missing data; Modeling; Nonparametric models; Parametric models; School dropouts; Sensitivity analysis; Statistical models; Statistics; bayesian – nonignorable – nonparametric

…NONPARAMETRIC BAYES: INFERENCE UNDER NONIGNORABLE MISSINGNESS AND MODEL SELECTION By Antonio Linero… …submodels, effectively giving tests of nonparametric models. Chapter 1 and Chapter 2 provide some… …hierarchical Dirichlet processes to construct tests of nonparametric models against semiparametric… …provide motivation for taking the Bayesian nonparametric approach to conducting inference on… …inference, nonparametric Bayesian methods appear to be the natural choice. Beyond flexible…

7 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Linero, A. R. (2015). Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0049143

Chicago Manual of Style (16^th Edition):

Linero, Antonio Richardo. “Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection.” 2015. Doctoral Dissertation, University of Florida. Accessed January 19, 2021. https://ufdc.ufl.edu/UFE0049143.

MLA Handbook (7^th Edition):

Linero, Antonio Richardo. “Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection.” 2015. Web. 19 Jan 2021.

Vancouver:

Linero AR. Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2021 Jan 19]. Available from: https://ufdc.ufl.edu/UFE0049143.

Council of Science Editors:

Linero AR. Nonparametric Bayes Inference under Nonignorable Missingness and Model Selection. [Doctoral Dissertation]. University of Florida; 2015. Available from: https://ufdc.ufl.edu/UFE0049143

University of Michigan

23. Lin, Danyu. Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model.

Degree: PhD, Biostatistics, 1989, University of Michigan

URL: http://hdl.handle.net/2027.42/128378

► The Cox proportional hazards model is a popular statistical tool for analyzing censored failure time data. It assumes the inclusion of all relevant covariates, the… (more)

▼ The Cox proportional hazards model is a popular statistical tool for analyzing censored failure time data. It assumes the inclusion of all relevant covariates, the log-linear dependence of the hazard function on covariates, and the multiplicative relationship between the baseline hazard function and the regression function of covariates. When these assumptions are violated, the conventional inference procedures based on the partial likelihood function can result in misleading statistical conclusions. However, the current statistical literature lacks convenient goodness-of-fit tests and robust methods for the general Cox method. In this dissertation, we identify two model-based consistent estimators for the inverse of the asymptotic covariance matrix of the maximum partial likelihood estimator of a general Cox model. Under the assumed model, the difference between these two estimators is shown to be asymptotically normal with mean zero and with a covariance matrix which can be consistently estimated. Global goodness-of-fit tests are then constructed based on these results. Extensive Monte Carlo studies indicate that the large-sample approximations to the null distributions of the new test statistics are fairly accurate for moderate-sized samples and that the new tests tend to be more powerful than several existing numerical methods. In addition, we establish the asymptotic normality of the maximum partial likelihood estimator under a possibly misspecified Cox proportional hazards model. A consistent covariance matrix estimator is suggested. For many misspecified Cox models, the asymptotic limit of the maximum partial likelihood estimator, or part of the limit, can be interpreted meaningfully so that the valid statistical inference about the corresponding covariate effects can be drawn based on the new asymptotic theory of the maximum partial likelihood estimator and the related results for the score statistics. Extensive studies demonstrate that the proposed robust tests and interval procedures are adequate for practical use. In contrast, the conventional model-based inference procedures often lead to tests with supranominal size and confidence intervals with poor coverage probability. The proposed robust procedures are finally extended to analyze multivariate incomplete failure time observations. Advisors/Committee Members: Brown, Morton B. (advisor), Wei, L. J. (advisor).

Subjects/Keywords: Cox; Fit; Goodness; Hazards; Model; Of; Proportional; Robust; Statistical Inference; Tests

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lin, D. (1989). Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/128378

Chicago Manual of Style (16^th Edition):

Lin, Danyu. “Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model.” 1989. Doctoral Dissertation, University of Michigan. Accessed January 19, 2021. http://hdl.handle.net/2027.42/128378.

MLA Handbook (7^th Edition):

Lin, Danyu. “Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model.” 1989. Web. 19 Jan 2021.

Vancouver:

Lin D. Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model. [Internet] [Doctoral dissertation]. University of Michigan; 1989. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2027.42/128378.

Council of Science Editors:

Lin D. Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model. [Doctoral Dissertation]. University of Michigan; 1989. Available from: http://hdl.handle.net/2027.42/128378

University of Hong Kong

24. 胡寶璇. Parametric inference for time series based upon goodness-of-fit.

Degree: 2001, University of Hong Kong

URL: http://hdl.handle.net/10722/33597

Subjects/Keywords: Time-series analysis - Statistical methods.; Inference.; Goodness-of-fit tests.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

胡寶璇. (2001). Parametric inference for time series based upon goodness-of-fit. (Thesis). University of Hong Kong. Retrieved from http://hdl.handle.net/10722/33597

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

胡寶璇. “Parametric inference for time series based upon goodness-of-fit.” 2001. Thesis, University of Hong Kong. Accessed January 19, 2021. http://hdl.handle.net/10722/33597.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

胡寶璇. “Parametric inference for time series based upon goodness-of-fit.” 2001. Web. 19 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

胡寶璇. Parametric inference for time series based upon goodness-of-fit. [Internet] [Thesis]. University of Hong Kong; 2001. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10722/33597.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

胡寶璇. Parametric inference for time series based upon goodness-of-fit. [Thesis]. University of Hong Kong; 2001. Available from: http://hdl.handle.net/10722/33597

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

25. Vegetabile, Brian Garrett. Methods for Optimal Covariate Balance in Observational Studies for Causal Inference.

Degree: Statistics, 2018, University of California – Irvine

URL: http://www.escholarship.org/uc/item/9f65n5h1

► The most basic approach to causal inference measures the response of a system or population to different exposures, or treatments, and compares one or more… (more)

▼ The most basic approach to causal inference measures the response of a system or population to different exposures, or treatments, and compares one or more summaries of the responses. Thinking formally about causal inference requires that we consider what the potential outcomes would have been under a set of alternative exposures. Though it is only possible in practice to observe a single outcome for each unit, the principles of good experimental design, including random treatment assignment, allow a valid comparison of average responses to each exposure because effects from extraneous factors are minimized. In observational settings, where exposures or treatments arise "naturally", i.e., without experimental manipulation, a common strategy for estimating causal effects is to find units that are similar based upon a set of covariates, but receiving different exposures, and then compare their outcomes. This strategy is challenging if there are many covariates. Balancing scores, a low-dimensional summary of the relevant covariate space, can facilitate causal inference for observational data in settings with many covariates. Propensity scores which measure the probability of receiving a particular exposure or treatment are one example of a balancing score. To estimate treatment effects, balancing scores are used to to group individuals from different exposure groups to compare their response levels, or functions of balancing scores are used to re-weight the sample. This thesis explores novel methods for obtaining covariate balance in observational studies through the use of balancing scores and weighting methodology. The dissertation begins by providing an overview of the potential outcome framework for causal inference in observational studies and required background knowledge for the methods developed. The first methodological contribution is the optimally balanced Gaussian process propensity score approach that applies a binary regression framework using Gaussian processes for estimating the propensity score. The hyperparameters of the process are selected to minimize a metric of covariate imbalance. The next methodological contribution is the development of targeted balancing weights for both binary and multi-treatment settings, where a covariate imbalance metric is created with respect to a covariate density of interest (this could be the distribution within the full population under study or within a specific subpopulation of interest) and unit weights are selected that minimize this metric, without an explicit assumption on the functional form of the weights. Each method is evaluated against competing methods from the causal inference literature through series of simulations and against a benchmark causal inference data set. The dissertation concludes with suggestions for future work. A contribution to measurement in observational studies is included as an appendix.

Subjects/Keywords: Statistics; Balancing Scores; Causal Inference; Nonparametric Estimation; Optimization; Propensity Scores

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APA (6^th Edition):

Vegetabile, B. G. (2018). Methods for Optimal Covariate Balance in Observational Studies for Causal Inference. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/9f65n5h1

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vegetabile, Brian Garrett. “Methods for Optimal Covariate Balance in Observational Studies for Causal Inference.” 2018. Thesis, University of California – Irvine. Accessed January 19, 2021. http://www.escholarship.org/uc/item/9f65n5h1.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vegetabile, Brian Garrett. “Methods for Optimal Covariate Balance in Observational Studies for Causal Inference.” 2018. Web. 19 Jan 2021.

Vancouver:

Vegetabile BG. Methods for Optimal Covariate Balance in Observational Studies for Causal Inference. [Internet] [Thesis]. University of California – Irvine; 2018. [cited 2021 Jan 19]. Available from: http://www.escholarship.org/uc/item/9f65n5h1.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vegetabile BG. Methods for Optimal Covariate Balance in Observational Studies for Causal Inference. [Thesis]. University of California – Irvine; 2018. Available from: http://www.escholarship.org/uc/item/9f65n5h1

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Ottawa

26. Han, Xuejun. On Nonparametric Bayesian Inference for Tukey Depth .

Degree: 2017, University of Ottawa

URL: http://hdl.handle.net/10393/36533

► The Dirichlet process is perhaps the most popular prior used in the nonparametric Bayesian inference. This prior which is placed on the space of probability… (more)

Subjects/Keywords: Nonparametric Bayesian Inference; Dirichlet Process; Data Depth; Tukey Depth

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Han, X. (2017). On Nonparametric Bayesian Inference for Tukey Depth . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/36533

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Han, Xuejun. “On Nonparametric Bayesian Inference for Tukey Depth .” 2017. Thesis, University of Ottawa. Accessed January 19, 2021. http://hdl.handle.net/10393/36533.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Han, Xuejun. “On Nonparametric Bayesian Inference for Tukey Depth .” 2017. Web. 19 Jan 2021.

Vancouver:

Han X. On Nonparametric Bayesian Inference for Tukey Depth . [Internet] [Thesis]. University of Ottawa; 2017. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10393/36533.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Han X. On Nonparametric Bayesian Inference for Tukey Depth . [Thesis]. University of Ottawa; 2017. Available from: http://hdl.handle.net/10393/36533

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

27. Marsh, Tracey Lynn. Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers.

Degree: PhD, 2017, University of Washington

URL: http://hdl.handle.net/1773/40505

► Recent advances in basic science, combined with new technologies that enable measurement of sophisticated biological processes, present numerous opportunities for advancing the clinical care of… (more)

Subjects/Keywords: Biomarkers; Clinical Decision Rules; Inference; Interaction; Net Benefit; Nonparametric; Biostatistics; Biostatistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marsh, T. L. (2017). Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40505

Chicago Manual of Style (16^th Edition):

Marsh, Tracey Lynn. “Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers.” 2017. Doctoral Dissertation, University of Washington. Accessed January 19, 2021. http://hdl.handle.net/1773/40505.

MLA Handbook (7^th Edition):

Marsh, Tracey Lynn. “Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers.” 2017. Web. 19 Jan 2021.

Vancouver:

Marsh TL. Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers. [Internet] [Doctoral dissertation]. University of Washington; 2017. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1773/40505.

Council of Science Editors:

Marsh TL. Distribution-Free Approaches to Assessing the Potential Clinical Impact of Biomarkers. [Doctoral Dissertation]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/40505

University of Toronto

28. Guimond, Tim Henry. A Nonparametric Bayesian Approach to Causal Modelling.

Degree: PhD, 2018, University of Toronto

URL: http://hdl.handle.net/1807/91855

► The Dirichlet process mixture regression (DPMR) method is a technique to produce a very flexible regression model using Bayesian principles based on data clusters. The… (more)

▼ The Dirichlet process mixture regression (DPMR) method is a technique to produce a very flexible regression model using Bayesian principles based on data clusters. The DPMR method begins by modelling the joint probability density for all variables in a problem. In observational studies, factors which influence treatment assignment (or treatment choice) may also be factors which influence outcomes. In such cases, we refer to these factors as confounders and standard estimates of treatment effects will be biased. Causal modelling approaches allow researchers to make causal inferences from observational data by accounting for confounding variables and thus correcting for the bias in unadjusted models. This thesis develops a fully Bayesian model where the Dirichlet process mixture models the joint distribution of all the variables of interest (confounders, treatment assignment and outcome), and is designed in such a way as to guarantee that this clustering approach adjusts for confounding while also providing a flexible model for outcomes. A local assumption of ignorability is required, as contrasted with the usual global assumption of strong ignorability, and the meaning and consequences of this alternate assumption are explored. The resulting model allows for inferences which are in accordance with causal model principles. In addition to estimating the overall average treatment effect (mean difference between two treatments), it also provides for the determination of conditional outcomes, hence can predict a region of the covariate space where one treatment dominates. Furthermore, the technique's capacity to examine the strongly ignorable assumption is demonstrated. This method can be harnessed to recreate the underlying counterfactual distributions that produce observational data and this is demonstrated with a simulated data set and its results are compared to other common approaches. Finally, the method is applied to a real life data set of an observational study of two possible methods of integrating mental health treatment into the shelter system for homeless men. This analysis of this data demonstrates a situation where treatments have identical outcomes for a subset of the covariate space and a subset of the space where one treatment clearly dominates, thereby informing an individualized patient driven approach to treatment selection. Advisors/Committee Members: Escobar, Michael D, Dalla Lana School of Public Health.

Subjects/Keywords: Bayesian; Causal inference; Dirichlet process; Nonparametric statistics; 0308

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Guimond, T. H. (2018). A Nonparametric Bayesian Approach to Causal Modelling. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/91855

Chicago Manual of Style (16^th Edition):

Guimond, Tim Henry. “A Nonparametric Bayesian Approach to Causal Modelling.” 2018. Doctoral Dissertation, University of Toronto. Accessed January 19, 2021. http://hdl.handle.net/1807/91855.

MLA Handbook (7^th Edition):

Guimond, Tim Henry. “A Nonparametric Bayesian Approach to Causal Modelling.” 2018. Web. 19 Jan 2021.

Vancouver:

Guimond TH. A Nonparametric Bayesian Approach to Causal Modelling. [Internet] [Doctoral dissertation]. University of Toronto; 2018. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1807/91855.

Council of Science Editors:

Guimond TH. A Nonparametric Bayesian Approach to Causal Modelling. [Doctoral Dissertation]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/91855

Duke University

29. Vogt, Erik. Essays on the Econometrics of Option Prices .

Degree: 2014, Duke University

URL: http://hdl.handle.net/10161/8692

► This dissertation develops new econometric techniques for use in estimating and conducting inference on parameters that can be identified from option prices. The techniques… (more)

▼ This dissertation develops new econometric techniques for use in estimating and conducting inference on parameters that can be identified from option prices. The techniques in question extend the existing literature in financial econometrics along several directions. The first essay considers the problem of estimating and conducting inference on the term structures of a class of economically interesting option portfolios. The option portfolios of interest play the role of functionals on an infinite-dimensional parameter (the option surface indexed by the term structure of state-price densities) that is well-known to be identified from option prices. Admissible functionals in the essay are generalizations of the VIX volatility index, which represent weighted integrals of options prices at a fixed maturity. By forming portfolios for various maturities, one can study their term structure. However, an important econometric difficulty that must be addressed is the illiquidity of options at longer maturities, which the essay overcomes by proposing a new nonparametric framework that takes advantage of asset pricing restrictions to estimate a shape-conforming option surface. In a second stage, the option portfolios of interest are cast as functionals of the estimated option surface, which then gives rise to a new, asymptotic distribution theory for option portfolios. The distribution theory is used to quantify the estimation error induced by computing integrated option portfolios from a sample of noisy option data. Moreover, by relying on the method of sieves, the framework is nonparametric, adheres to economic shape restrictions for arbitrary maturities, yields closed-form option prices, and is easy to compute. The framework also permits the extraction of the entire term structure of risk-neutral distributions in closed-form. Monte Carlo simulations confirm the framework's performance in finite samples. An application to the term structure of the synthetic variance swap portfolio finds sizeable uncertainty around the swap's true fair value, particularly when the variance swap is synthesized from noisy long-maturity options. A nonparametric investigation into the term structure of the variance risk premium finds growing compensation for variance risk at long maturities. The second essay, which represents joint work with Jia Li, proposes an econometric framework for inference on parametric option pricing models with two novel features. First, point identification is not assumed. The lack of identification arises naturally when a researcher only has interval observations on option quotes rather than on the efficient option price itself, which implies that the parameters of interest are only partially identified by observed option prices. This issue is solved by adopting a moment inequality approach. Second, the essay imposes no-arbitrage restrictions between the risk-neutral and the physical measures by nonparametrically estimating quantities that are invariant to changes of measures using high-frequency returns… Advisors/Committee Members: Tauchen, George (advisor).

Subjects/Keywords: Economics; Finance; Inference; Nonparametric; Option Pricing; Sieve Estimation; State-Price Density

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vogt, E. (2014). Essays on the Econometrics of Option Prices . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/8692

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vogt, Erik. “Essays on the Econometrics of Option Prices .” 2014. Thesis, Duke University. Accessed January 19, 2021. http://hdl.handle.net/10161/8692.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vogt, Erik. “Essays on the Econometrics of Option Prices .” 2014. Web. 19 Jan 2021.

Vancouver:

Vogt E. Essays on the Econometrics of Option Prices . [Internet] [Thesis]. Duke University; 2014. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10161/8692.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vogt E. Essays on the Econometrics of Option Prices . [Thesis]. Duke University; 2014. Available from: http://hdl.handle.net/10161/8692

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

30. Tang, Chuan-Fa. Nonparametric Inference for Orderings and Associations Between two Random Variables.

Degree: PhD, Statistics, 2017, University of South Carolina

URL: https://scholarcommons.sc.edu/etd/4356

► Ordering and dependency are two aspects to describe the relationship between two random variables. In this thesis, we choose two hypothesis testing problems to… (more)

Subjects/Keywords: Physical Sciences and Mathematics; Statistics and Probability; Nonparametric Inference; Random Variables

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tang, C. (2017). Nonparametric Inference for Orderings and Associations Between two Random Variables. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/4356

Chicago Manual of Style (16^th Edition):

Tang, Chuan-Fa. “Nonparametric Inference for Orderings and Associations Between two Random Variables.” 2017. Doctoral Dissertation, University of South Carolina. Accessed January 19, 2021. https://scholarcommons.sc.edu/etd/4356.

MLA Handbook (7^th Edition):

Tang, Chuan-Fa. “Nonparametric Inference for Orderings and Associations Between two Random Variables.” 2017. Web. 19 Jan 2021.

Vancouver:

Tang C. Nonparametric Inference for Orderings and Associations Between two Random Variables. [Internet] [Doctoral dissertation]. University of South Carolina; 2017. [cited 2021 Jan 19]. Available from: https://scholarcommons.sc.edu/etd/4356.

Council of Science Editors:

Tang C. Nonparametric Inference for Orderings and Associations Between two Random Variables. [Doctoral Dissertation]. University of South Carolina; 2017. Available from: https://scholarcommons.sc.edu/etd/4356

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