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You searched for subject:(Penalized model selection). Showing records 1 – 15 of 15 total matches.

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Bowling Green State University

1. Yousef, Mohammed A. Two-Stage SCAD Lasso for Linear Mixed Model Selection.

Degree: PhD, Statistics, 2019, Bowling Green State University

 Linear regression model is the classical approach to explain the relationship between the response variable (dependent) and predictors (independent). However, when the number of predictors… (more)

Subjects/Keywords: Statistics; Mixed model selection; SCAD Lasso; Linear mixed model; Penalized model selection; two-stage model selection

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

APA (6th Edition):

Yousef, M. A. (2019). Two-Stage SCAD Lasso for Linear Mixed Model Selection. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879

Chicago Manual of Style (16th Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Doctoral Dissertation, Bowling Green State University. Accessed August 24, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

MLA Handbook (7th Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Web. 24 Aug 2019.

Vancouver:

Yousef MA. Two-Stage SCAD Lasso for Linear Mixed Model Selection. [Internet] [Doctoral dissertation]. Bowling Green State University; 2019. [cited 2019 Aug 24]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

Council of Science Editors:

Yousef MA. Two-Stage SCAD Lasso for Linear Mixed Model Selection. [Doctoral Dissertation]. Bowling Green State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879


Université Catholique de Louvain

2. Jaeger, Jonathan. Functional estimation in systems defined by differential equations using Bayesian smoothing methods.

Degree: 2012, Université Catholique de Louvain

Ordinary differential equations (ODEs) are widely used to model physical, chemical and biological processes. Currently, the most commonly used estimation procedures rely on nonlinear least… (more)

Subjects/Keywords: Bayesian ODE-penalized B-spline; ODE-model selection; Ordinary differential equations

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APA (6th Edition):

Jaeger, J. (2012). Functional estimation in systems defined by differential equations using Bayesian smoothing methods. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/115164

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

Chicago Manual of Style (16th Edition):

Jaeger, Jonathan. “Functional estimation in systems defined by differential equations using Bayesian smoothing methods.” 2012. Thesis, Université Catholique de Louvain. Accessed August 24, 2019. http://hdl.handle.net/2078.1/115164.

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

MLA Handbook (7th Edition):

Jaeger, Jonathan. “Functional estimation in systems defined by differential equations using Bayesian smoothing methods.” 2012. Web. 24 Aug 2019.

Vancouver:

Jaeger J. Functional estimation in systems defined by differential equations using Bayesian smoothing methods. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/2078.1/115164.

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

Council of Science Editors:

Jaeger J. Functional estimation in systems defined by differential equations using Bayesian smoothing methods. [Thesis]. Université Catholique de Louvain; 2012. Available from: http://hdl.handle.net/2078.1/115164

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


Duke University

3. Shi, Minghui. Bayesian Sparse Learning for High Dimensional Data .

Degree: 2011, Duke University

  In this thesis, we develop some Bayesian sparse learning methods for high dimensional data analysis. There are two important topics that are related to… (more)

Subjects/Keywords: Statistics; Factor model; High dimensional Data; penalized marginal likelihood; Variable Selection

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APA (6th Edition):

Shi, M. (2011). Bayesian Sparse Learning for High Dimensional Data . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/3869

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

Chicago Manual of Style (16th Edition):

Shi, Minghui. “Bayesian Sparse Learning for High Dimensional Data .” 2011. Thesis, Duke University. Accessed August 24, 2019. http://hdl.handle.net/10161/3869.

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

MLA Handbook (7th Edition):

Shi, Minghui. “Bayesian Sparse Learning for High Dimensional Data .” 2011. Web. 24 Aug 2019.

Vancouver:

Shi M. Bayesian Sparse Learning for High Dimensional Data . [Internet] [Thesis]. Duke University; 2011. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/10161/3869.

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

Council of Science Editors:

Shi M. Bayesian Sparse Learning for High Dimensional Data . [Thesis]. Duke University; 2011. Available from: http://hdl.handle.net/10161/3869

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


University of Western Ontario

4. Xu, Changjiang. Model Selection with Information Criteria.

Degree: 2010, University of Western Ontario

 This thesis is on model selection using information criteria. The information criteria include generalized information criterion and a family of Bayesian information criteria. The properties… (more)

Subjects/Keywords: Statistical modeling; model selection; variable selection; penalized likelihood; model selection criterion; information criteria; Statistics and Probability

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APA (6th Edition):

Xu, C. (2010). Model Selection with Information Criteria. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/46

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

Chicago Manual of Style (16th Edition):

Xu, Changjiang. “Model Selection with Information Criteria.” 2010. Thesis, University of Western Ontario. Accessed August 24, 2019. https://ir.lib.uwo.ca/etd/46.

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

MLA Handbook (7th Edition):

Xu, Changjiang. “Model Selection with Information Criteria.” 2010. Web. 24 Aug 2019.

Vancouver:

Xu C. Model Selection with Information Criteria. [Internet] [Thesis]. University of Western Ontario; 2010. [cited 2019 Aug 24]. Available from: https://ir.lib.uwo.ca/etd/46.

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

Council of Science Editors:

Xu C. Model Selection with Information Criteria. [Thesis]. University of Western Ontario; 2010. Available from: https://ir.lib.uwo.ca/etd/46

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


University of Iowa

5. Liu, Li. Grouped variable selection in high dimensional partially linear additive Cox model.

Degree: PhD, Biostatistics, 2010, University of Iowa

  In the analysis of survival outcome supplemented with both clinical information and high-dimensional gene expression data, traditional Cox proportional hazard model fails to meet… (more)

Subjects/Keywords: adaptive group lasso; Cox Model; grouped variable selection; group lasso; high dimensional; penalized regression; Biostatistics

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APA (6th Edition):

Liu, L. (2010). Grouped variable selection in high dimensional partially linear additive Cox model. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/847

Chicago Manual of Style (16th Edition):

Liu, Li. “Grouped variable selection in high dimensional partially linear additive Cox model.” 2010. Doctoral Dissertation, University of Iowa. Accessed August 24, 2019. https://ir.uiowa.edu/etd/847.

MLA Handbook (7th Edition):

Liu, Li. “Grouped variable selection in high dimensional partially linear additive Cox model.” 2010. Web. 24 Aug 2019.

Vancouver:

Liu L. Grouped variable selection in high dimensional partially linear additive Cox model. [Internet] [Doctoral dissertation]. University of Iowa; 2010. [cited 2019 Aug 24]. Available from: https://ir.uiowa.edu/etd/847.

Council of Science Editors:

Liu L. Grouped variable selection in high dimensional partially linear additive Cox model. [Doctoral Dissertation]. University of Iowa; 2010. Available from: https://ir.uiowa.edu/etd/847


Colorado State University

6. McConville, Kelly. Improved estimation for complex surveys using modern regression techniques.

Degree: PhD, Statistics, 2007, Colorado State University

 In the field of survey statistics, finite population quantities are often estimated based on complex survey data. In this thesis, estimation of the finite population… (more)

Subjects/Keywords: penalized splines; non-parametrics; survey; lasso; model selection

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APA (6th Edition):

McConville, K. (2007). Improved estimation for complex surveys using modern regression techniques. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/48159

Chicago Manual of Style (16th Edition):

McConville, Kelly. “Improved estimation for complex surveys using modern regression techniques.” 2007. Doctoral Dissertation, Colorado State University. Accessed August 24, 2019. http://hdl.handle.net/10217/48159.

MLA Handbook (7th Edition):

McConville, Kelly. “Improved estimation for complex surveys using modern regression techniques.” 2007. Web. 24 Aug 2019.

Vancouver:

McConville K. Improved estimation for complex surveys using modern regression techniques. [Internet] [Doctoral dissertation]. Colorado State University; 2007. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/10217/48159.

Council of Science Editors:

McConville K. Improved estimation for complex surveys using modern regression techniques. [Doctoral Dissertation]. Colorado State University; 2007. Available from: http://hdl.handle.net/10217/48159


University of Iowa

7. Jiao, Feiran. High-dimensional inference of ordinal data with medical applications.

Degree: PhD, Statistics, 2016, University of Iowa

  Ordinal response variables abound in scientific and quantitative analyses, whose outcomes comprise a few categorical values that admit a natural ordering, so that their… (more)

Subjects/Keywords: bi-level variable selection; composite bridge penalty; cumulative link model; lung image data; MM algorithm; penalized maximum likelihood

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APA (6th Edition):

Jiao, F. (2016). High-dimensional inference of ordinal data with medical applications. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6150

Chicago Manual of Style (16th Edition):

Jiao, Feiran. “High-dimensional inference of ordinal data with medical applications.” 2016. Doctoral Dissertation, University of Iowa. Accessed August 24, 2019. https://ir.uiowa.edu/etd/6150.

MLA Handbook (7th Edition):

Jiao, Feiran. “High-dimensional inference of ordinal data with medical applications.” 2016. Web. 24 Aug 2019.

Vancouver:

Jiao F. High-dimensional inference of ordinal data with medical applications. [Internet] [Doctoral dissertation]. University of Iowa; 2016. [cited 2019 Aug 24]. Available from: https://ir.uiowa.edu/etd/6150.

Council of Science Editors:

Jiao F. High-dimensional inference of ordinal data with medical applications. [Doctoral Dissertation]. University of Iowa; 2016. Available from: https://ir.uiowa.edu/etd/6150


University of Michigan

8. Qian, Min. Model Selection and l1 Penalization for Individualized Treatment Rules.

Degree: PhD, Statistics, 2010, University of Michigan

 Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients. An individualized treatment rule is a decision rule… (more)

Subjects/Keywords: Individualized Treatment Rule; Decision Making; Model Selection; L1 Penalized Least Sqaures; Statistics and Numeric Data; Science

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APA (6th Edition):

Qian, M. (2010). Model Selection and l1 Penalization for Individualized Treatment Rules. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77919

Chicago Manual of Style (16th Edition):

Qian, Min. “Model Selection and l1 Penalization for Individualized Treatment Rules.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 24, 2019. http://hdl.handle.net/2027.42/77919.

MLA Handbook (7th Edition):

Qian, Min. “Model Selection and l1 Penalization for Individualized Treatment Rules.” 2010. Web. 24 Aug 2019.

Vancouver:

Qian M. Model Selection and l1 Penalization for Individualized Treatment Rules. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/2027.42/77919.

Council of Science Editors:

Qian M. Model Selection and l1 Penalization for Individualized Treatment Rules. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77919

9. Thouvenot, Vincent. Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting.

Degree: Docteur es, Mathématiques appliquées, 2015, Paris Saclay

L'électricité ne se stockant pas aisément, EDF a besoin d'outils de prévision de consommation et de production efficaces. Le développement de nouvelles méthodes automatiques de… (more)

Subjects/Keywords: Statistique; Modèle additif; Méthode pénalisée; Estimateurs en plusieurs étapes; Prévision de consommation électrique; Selection; Statistic; Additive model; Penalized method; Multi-Step estimator; Electricity load forecasting; Selection

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APA (6th Edition):

Thouvenot, V. (2015). Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2015SACLS184

Chicago Manual of Style (16th Edition):

Thouvenot, Vincent. “Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting.” 2015. Doctoral Dissertation, Paris Saclay. Accessed August 24, 2019. http://www.theses.fr/2015SACLS184.

MLA Handbook (7th Edition):

Thouvenot, Vincent. “Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting.” 2015. Web. 24 Aug 2019.

Vancouver:

Thouvenot V. Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting. [Internet] [Doctoral dissertation]. Paris Saclay; 2015. [cited 2019 Aug 24]. Available from: http://www.theses.fr/2015SACLS184.

Council of Science Editors:

Thouvenot V. Estimation et sélection pour les modèles additifs et application à la prévision de la consommation électrique : Estimation and selection in additive models and application to load demand forecasting. [Doctoral Dissertation]. Paris Saclay; 2015. Available from: http://www.theses.fr/2015SACLS184

10. Ollier, Edouard. Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data.

Degree: Docteur es, Mathématiques, 2017, Lyon

Cette thèse est principalement consacrée au développement de méthodes de sélection de modèles par maximum de vraisemblance pénalisée dans le cadre de données complexes. Un… (more)

Subjects/Keywords: Sélection de modèle; Vraisemblance pénalisée; Algorithme SAEM; Algorithmes gradient proximaux; Modèles non linéaires à effets mixtes; Model selection; Penalized likelihood; SAEM algorithm; Proximal gradient algorithm; Non linear mixed effects models

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APA (6th Edition):

Ollier, E. (2017). Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2017LYSEN097

Chicago Manual of Style (16th Edition):

Ollier, Edouard. “Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data.” 2017. Doctoral Dissertation, Lyon. Accessed August 24, 2019. http://www.theses.fr/2017LYSEN097.

MLA Handbook (7th Edition):

Ollier, Edouard. “Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data.” 2017. Web. 24 Aug 2019.

Vancouver:

Ollier E. Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data. [Internet] [Doctoral dissertation]. Lyon; 2017. [cited 2019 Aug 24]. Available from: http://www.theses.fr/2017LYSEN097.

Council of Science Editors:

Ollier E. Sélection de modèles statistiques par méthodes de vraisemblance pénalisée pour l'étude de données complexes : Statistical Model Selection by penalized likelihood method for the study of complex data. [Doctoral Dissertation]. Lyon; 2017. Available from: http://www.theses.fr/2017LYSEN097

11. Shi, Fei. Model Selection in Multivariate Analysis with Missing Data.

Degree: 2013, University of Illinois – Chicago

 A new model selection algorithm based on maximizing penalized likelihood function with the smoothly clipped absolute deviation (SCAD) penalty function is developed for missing data… (more)

Subjects/Keywords: EM algorithm; Missing data; Model selection; Penalized likelihood; Regression; SCAD

…Current model selection method in missing data problems iteratively optimizes the penalized Q… …Continued) MPLE Maximum Penalized Likelihood Estimates. MRME Median of Ratio of Model… …Deviation. xi SUMMARY In this dissertation, a new model selection algorithm based on… …expensive process. We proposed a new model selection algorithm that utilized an approximation… …parameter for the penalty function. Furthermore, we proposed a new model selection scheme that not… 

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APA (6th Edition):

Shi, F. (2013). Model Selection in Multivariate Analysis with Missing Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9760

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

Chicago Manual of Style (16th Edition):

Shi, Fei. “Model Selection in Multivariate Analysis with Missing Data.” 2013. Thesis, University of Illinois – Chicago. Accessed August 24, 2019. http://hdl.handle.net/10027/9760.

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

MLA Handbook (7th Edition):

Shi, Fei. “Model Selection in Multivariate Analysis with Missing Data.” 2013. Web. 24 Aug 2019.

Vancouver:

Shi F. Model Selection in Multivariate Analysis with Missing Data. [Internet] [Thesis]. University of Illinois – Chicago; 2013. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/10027/9760.

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

Council of Science Editors:

Shi F. Model Selection in Multivariate Analysis with Missing Data. [Thesis]. University of Illinois – Chicago; 2013. Available from: http://hdl.handle.net/10027/9760

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

12. LIN NAN. A penalized likelihood approach in covariance graphical model selection.

Degree: 2010, National University of Singapore

Subjects/Keywords: Penalized likelihood; covariance matrix; covariance graphical model selection; sparsistency; consistency; LASSO

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APA (6th Edition):

NAN, L. (2010). A penalized likelihood approach in covariance graphical model selection. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/22867

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

Chicago Manual of Style (16th Edition):

NAN, LIN. “A penalized likelihood approach in covariance graphical model selection.” 2010. Thesis, National University of Singapore. Accessed August 24, 2019. http://scholarbank.nus.edu.sg/handle/10635/22867.

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

MLA Handbook (7th Edition):

NAN, LIN. “A penalized likelihood approach in covariance graphical model selection.” 2010. Web. 24 Aug 2019.

Vancouver:

NAN L. A penalized likelihood approach in covariance graphical model selection. [Internet] [Thesis]. National University of Singapore; 2010. [cited 2019 Aug 24]. Available from: http://scholarbank.nus.edu.sg/handle/10635/22867.

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

Council of Science Editors:

NAN L. A penalized likelihood approach in covariance graphical model selection. [Thesis]. National University of Singapore; 2010. Available from: http://scholarbank.nus.edu.sg/handle/10635/22867

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


University of Iowa

13. Liu, Hai. Semiparametric regression analysis of zero-inflated data.

Degree: PhD, Statistics, 2009, University of Iowa

  Zero-inflated data abound in ecological studies as well as in other scientific and quantitative fields. Nonparametric regression with zero-inflated response may be studied via… (more)

Subjects/Keywords: Asymptotic normality; Constrained model; EM algorithm; Model selection; Penalized likelihood; Threshold model; Statistics and Probability

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APA (6th Edition):

Liu, H. (2009). Semiparametric regression analysis of zero-inflated data. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/308

Chicago Manual of Style (16th Edition):

Liu, Hai. “Semiparametric regression analysis of zero-inflated data.” 2009. Doctoral Dissertation, University of Iowa. Accessed August 24, 2019. https://ir.uiowa.edu/etd/308.

MLA Handbook (7th Edition):

Liu, Hai. “Semiparametric regression analysis of zero-inflated data.” 2009. Web. 24 Aug 2019.

Vancouver:

Liu H. Semiparametric regression analysis of zero-inflated data. [Internet] [Doctoral dissertation]. University of Iowa; 2009. [cited 2019 Aug 24]. Available from: https://ir.uiowa.edu/etd/308.

Council of Science Editors:

Liu H. Semiparametric regression analysis of zero-inflated data. [Doctoral Dissertation]. University of Iowa; 2009. Available from: https://ir.uiowa.edu/etd/308


Virginia Commonwealth University

14. Kong, XiangRong. Variable Selection in Competing Risks Using the L1-Penalized Cox Model.

Degree: PhD, Biostatistics, 2008, Virginia Commonwealth University

 One situation in survival analysis is that the failure of an individual can happen because of one of multiple distinct causes. Survival data generated in… (more)

Subjects/Keywords: Cox model; Variable selection; Penalized model; Competing risks; Biostatistics; Physical Sciences and Mathematics; Statistics and Probability

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APA (6th Edition):

Kong, X. (2008). Variable Selection in Competing Risks Using the L1-Penalized Cox Model. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/1638

Chicago Manual of Style (16th Edition):

Kong, XiangRong. “Variable Selection in Competing Risks Using the L1-Penalized Cox Model.” 2008. Doctoral Dissertation, Virginia Commonwealth University. Accessed August 24, 2019. https://scholarscompass.vcu.edu/etd/1638.

MLA Handbook (7th Edition):

Kong, XiangRong. “Variable Selection in Competing Risks Using the L1-Penalized Cox Model.” 2008. Web. 24 Aug 2019.

Vancouver:

Kong X. Variable Selection in Competing Risks Using the L1-Penalized Cox Model. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2008. [cited 2019 Aug 24]. Available from: https://scholarscompass.vcu.edu/etd/1638.

Council of Science Editors:

Kong X. Variable Selection in Competing Risks Using the L1-Penalized Cox Model. [Doctoral Dissertation]. Virginia Commonwealth University; 2008. Available from: https://scholarscompass.vcu.edu/etd/1638

15. Vasseur, Yann. Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups.

Degree: Docteur es, Mathématiques appliquées, 2017, Paris Saclay

Dans cette thèse, nous cherchons à caractériser les facteurs de transcription de la plante Arabidopsis thaliana, gènes importants pour la régulation de l'expression du génome.… (more)

Subjects/Keywords: Grande dimension; Réseaux de gènes; Sélection de modèles; Régression pénalisée; Classification de graphes orientés; Indices de comparaison de couples de partitions; High dimension; Gene networks; Model selection; Penalized regression; Directed graphs clustering; Comparison index for pairs of partitions

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APA (6th Edition):

Vasseur, Y. (2017). Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2017SACLS475

Chicago Manual of Style (16th Edition):

Vasseur, Yann. “Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups.” 2017. Doctoral Dissertation, Paris Saclay. Accessed August 24, 2019. http://www.theses.fr/2017SACLS475.

MLA Handbook (7th Edition):

Vasseur, Yann. “Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups.” 2017. Web. 24 Aug 2019.

Vancouver:

Vasseur Y. Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups. [Internet] [Doctoral dissertation]. Paris Saclay; 2017. [cited 2019 Aug 24]. Available from: http://www.theses.fr/2017SACLS475.

Council of Science Editors:

Vasseur Y. Inférence de réseaux de régulation orientés pour les facteurs de transcription d'Arabidopsis thaliana et création de groupes de co-régulation : Inference of directed regulatory networks on the transcription factors of Arabidopsis thaliana and setting up of co-regulation groups. [Doctoral Dissertation]. Paris Saclay; 2017. Available from: http://www.theses.fr/2017SACLS475

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