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You searched for subject:(High dimensional estimation). Showing records 1 – 29 of 29 total matches.

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University of Minnesota

1. Chen, Sheng. Computational and Statistical Aspects of High-Dimensional Structured Estimation.

Degree: PhD, Computer Science, 2018, University of Minnesota

 Modern statistical learning often faces high-dimensional data, for which the number of features that should be considered is very large. In consideration of various constraints… (more)

Subjects/Keywords: High-Dimensional Statistics; Machine Learning; Structured Estimation

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

APA (6th Edition):

Chen, S. (2018). Computational and Statistical Aspects of High-Dimensional Structured Estimation. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/198991

Chicago Manual of Style (16th Edition):

Chen, Sheng. “Computational and Statistical Aspects of High-Dimensional Structured Estimation.” 2018. Doctoral Dissertation, University of Minnesota. Accessed March 23, 2019. http://hdl.handle.net/11299/198991.

MLA Handbook (7th Edition):

Chen, Sheng. “Computational and Statistical Aspects of High-Dimensional Structured Estimation.” 2018. Web. 23 Mar 2019.

Vancouver:

Chen S. Computational and Statistical Aspects of High-Dimensional Structured Estimation. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/11299/198991.

Council of Science Editors:

Chen S. Computational and Statistical Aspects of High-Dimensional Structured Estimation. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/198991


University of Texas – Austin

2. Gunasekar, Suriya. Mining structured matrices in high dimensions.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

 Structured matrices refer to matrix valued data that are embedded in an inherent lower dimensional manifold with smaller degrees of freedom compared to the ambient… (more)

Subjects/Keywords: Matrix completion; High dimensional estimation; EHRs; Letor; Matrix estimation; Sample complexity

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APA (6th Edition):

Gunasekar, S. (2016). Mining structured matrices in high dimensions. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43772

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):

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Thesis, University of Texas – Austin. Accessed March 23, 2019. http://hdl.handle.net/2152/43772.

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

MLA Handbook (7th Edition):

Gunasekar, Suriya. “Mining structured matrices in high dimensions.” 2016. Web. 23 Mar 2019.

Vancouver:

Gunasekar S. Mining structured matrices in high dimensions. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2152/43772.

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

Council of Science Editors:

Gunasekar S. Mining structured matrices in high dimensions. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43772

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


Princeton University

3. Bose, Koushiki. Robust Dependence-Adjusted Methods for High Dimensional Data .

Degree: PhD, 2018, Princeton University

 The focus of this dissertation is the development, implementation and verification of robust methods for high dimensional heavy-tailed data, with an emphasis on underlying dependence-adjustment… (more)

Subjects/Keywords: Dependence Adjustment; Factor Models; High Dimensional Data; Robust Estimation; R package

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APA (6th Edition):

Bose, K. (2018). Robust Dependence-Adjusted Methods for High Dimensional Data . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

Chicago Manual of Style (16th Edition):

Bose, Koushiki. “Robust Dependence-Adjusted Methods for High Dimensional Data .” 2018. Doctoral Dissertation, Princeton University. Accessed March 23, 2019. http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d.

MLA Handbook (7th Edition):

Bose, Koushiki. “Robust Dependence-Adjusted Methods for High Dimensional Data .” 2018. Web. 23 Mar 2019.

Vancouver:

Bose K. Robust Dependence-Adjusted Methods for High Dimensional Data . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Mar 23]. Available from: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d.

Council of Science Editors:

Bose K. Robust Dependence-Adjusted Methods for High Dimensional Data . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

4. Shou, Haochang. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.

Degree: 2014, Johns Hopkins University

 The thesis investigates a specific type of functional data with multilevel structures induced by complex experimental designs. Novel statistical methods based on principal component analysis… (more)

Subjects/Keywords: functional data analysis; multilevel and structured data; high-dimensional data; imaging reproducibility; shrinkage estimation

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APA (6th Edition):

Shou, H. (2014). Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/37867

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):

Shou, Haochang. “Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.” 2014. Thesis, Johns Hopkins University. Accessed March 23, 2019. http://jhir.library.jhu.edu/handle/1774.2/37867.

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

MLA Handbook (7th Edition):

Shou, Haochang. “Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.” 2014. Web. 23 Mar 2019.

Vancouver:

Shou H. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. [Internet] [Thesis]. Johns Hopkins University; 2014. [cited 2019 Mar 23]. Available from: http://jhir.library.jhu.edu/handle/1774.2/37867.

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

Council of Science Editors:

Shou H. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. [Thesis]. Johns Hopkins University; 2014. Available from: http://jhir.library.jhu.edu/handle/1774.2/37867

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


University of California – Berkeley

5. Zhu, Ying. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.

Degree: Business Administration, Ph, 2015, University of California – Berkeley

 Econometric models based on observational data are often endogenous due to measurement error, autocorrelated errors, simultaneity and omitted variables, non-random sampling, self-selection, etc. Parameter estimates… (more)

Subjects/Keywords: Statistics; Economics; High-dimensional statistics; Lasso; sample selection; semiparametric estimation; sparsity; variable selection

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APA (6th Edition):

Zhu, Y. (2015). Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9vw1524p

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):

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Thesis, University of California – Berkeley. Accessed March 23, 2019. http://www.escholarship.org/uc/item/9vw1524p.

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

MLA Handbook (7th Edition):

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Web. 23 Mar 2019.

Vancouver:

Zhu Y. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2019 Mar 23]. Available from: http://www.escholarship.org/uc/item/9vw1524p.

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

Council of Science Editors:

Zhu Y. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/9vw1524p

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


University of Washington

6. Yang, Miaoyu. Essays on Machine Learning and Hedonic Models.

Degree: PhD, 2016, University of Washington

 Chapter 1 and 2: We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. We derive… (more)

Subjects/Keywords: Applied Microeconometrics; Demand Estimation; Environmental Economics; Hedonic Models; High Dimensional Data; Machine Learning; Economics; economics

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APA (6th Edition):

Yang, M. (2016). Essays on Machine Learning and Hedonic Models. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/37084

Chicago Manual of Style (16th Edition):

Yang, Miaoyu. “Essays on Machine Learning and Hedonic Models.” 2016. Doctoral Dissertation, University of Washington. Accessed March 23, 2019. http://hdl.handle.net/1773/37084.

MLA Handbook (7th Edition):

Yang, Miaoyu. “Essays on Machine Learning and Hedonic Models.” 2016. Web. 23 Mar 2019.

Vancouver:

Yang M. Essays on Machine Learning and Hedonic Models. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1773/37084.

Council of Science Editors:

Yang M. Essays on Machine Learning and Hedonic Models. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/37084


ETH Zürich

7. Stucky, Benjamin. Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity.

Degree: 2017, ETH Zürich

 To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern statistics, machine learning and in particular for high dimensional(more)

Subjects/Keywords: High-dimensional regression; Sparsity; Penalized estimation; Sharp oracle inequality; Asymptotic confidence intervalls

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APA (6th Edition):

Stucky, B. (2017). Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/197854

Chicago Manual of Style (16th Edition):

Stucky, Benjamin. “Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity.” 2017. Doctoral Dissertation, ETH Zürich. Accessed March 23, 2019. http://hdl.handle.net/20.500.11850/197854.

MLA Handbook (7th Edition):

Stucky, Benjamin. “Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity.” 2017. Web. 23 Mar 2019.

Vancouver:

Stucky B. Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/20.500.11850/197854.

Council of Science Editors:

Stucky B. Asymptotic Confidence Regions and Sharp Oracle Results under Structured Sparsity. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/197854

8. Bun, Joël. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.

Degree: Docteur es, Physique, 2016, Paris Saclay

De nos jours, il est de plus en plus fréquent de travailler sur des bases de données de très grandes tailles dans plein de domaines… (more)

Subjects/Keywords: Matrices aléatoires; Statistiques en grande dimension; Estimation; Décomposition Spectrale; Random matrices; High dimensional statistics; Estimation; Spectral decomposition

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APA (6th Edition):

Bun, J. (2016). Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLS245

Chicago Manual of Style (16th Edition):

Bun, Joël. “Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.” 2016. Doctoral Dissertation, Paris Saclay. Accessed March 23, 2019. http://www.theses.fr/2016SACLS245.

MLA Handbook (7th Edition):

Bun, Joël. “Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.” 2016. Web. 23 Mar 2019.

Vancouver:

Bun J. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Mar 23]. Available from: http://www.theses.fr/2016SACLS245.

Council of Science Editors:

Bun J. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLS245


University of Michigan

9. Meng, Zhaoshi. Distributed Learning, Prediction and Detection in Probabilistic Graphs.

Degree: PhD, Electrical Engineering: Systems, 2014, University of Michigan

 Critical to high-dimensional statistical estimation is to exploit the structure in the data distribution. Probabilistic graphical models provide an efficient framework for representing complex joint… (more)

Subjects/Keywords: probabilistic graphical models; machine learning; high-dimensional statistics; statistical estimation; distributed learning and estimation; Computer Science; Engineering

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APA (6th Edition):

Meng, Z. (2014). Distributed Learning, Prediction and Detection in Probabilistic Graphs. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/110499

Chicago Manual of Style (16th Edition):

Meng, Zhaoshi. “Distributed Learning, Prediction and Detection in Probabilistic Graphs.” 2014. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/110499.

MLA Handbook (7th Edition):

Meng, Zhaoshi. “Distributed Learning, Prediction and Detection in Probabilistic Graphs.” 2014. Web. 23 Mar 2019.

Vancouver:

Meng Z. Distributed Learning, Prediction and Detection in Probabilistic Graphs. [Internet] [Doctoral dissertation]. University of Michigan; 2014. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/110499.

Council of Science Editors:

Meng Z. Distributed Learning, Prediction and Detection in Probabilistic Graphs. [Doctoral Dissertation]. University of Michigan; 2014. Available from: http://hdl.handle.net/2027.42/110499


Texas A&M University

10. Song, Juhee. Bootstrapping in a high dimensional but very low sample size problem.

Degree: 2006, Texas A&M University

High Dimension, Low Sample Size (HDLSS) problems have received much attention recently in many areas of science. Analysis of microarray experiments is one such area.… (more)

Subjects/Keywords: Bootstrap; Density Estimation; Clustering; High dimensional Data

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APA (6th Edition):

Song, J. (2006). Bootstrapping in a high dimensional but very low sample size problem. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/3853

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):

Song, Juhee. “Bootstrapping in a high dimensional but very low sample size problem.” 2006. Thesis, Texas A&M University. Accessed March 23, 2019. http://hdl.handle.net/1969.1/3853.

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

MLA Handbook (7th Edition):

Song, Juhee. “Bootstrapping in a high dimensional but very low sample size problem.” 2006. Web. 23 Mar 2019.

Vancouver:

Song J. Bootstrapping in a high dimensional but very low sample size problem. [Internet] [Thesis]. Texas A&M University; 2006. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/1969.1/3853.

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

Council of Science Editors:

Song J. Bootstrapping in a high dimensional but very low sample size problem. [Thesis]. Texas A&M University; 2006. Available from: http://hdl.handle.net/1969.1/3853

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


University of Western Australia

11. Feher, Kristen. Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach.

Degree: PhD, 2010, University of Western Australia

The aim of genomic data analysis is to infer specific relationships amongst constituents of a complex system. Applied statistical methodology that was accordingly developed rely… (more)

Subjects/Keywords: Genomics; Bioinformatics; Analysis of covariance; Collineation; Random matrix theory; Bioinformatics; Microarray data; High dimensional inference; Clustering; Covariance estimation

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APA (6th Edition):

Feher, K. (2010). Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach. (Doctoral Dissertation). University of Western Australia. Retrieved from http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=29496&local_base=GEN01-INS01

Chicago Manual of Style (16th Edition):

Feher, Kristen. “Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach.” 2010. Doctoral Dissertation, University of Western Australia. Accessed March 23, 2019. http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=29496&local_base=GEN01-INS01.

MLA Handbook (7th Edition):

Feher, Kristen. “Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach.” 2010. Web. 23 Mar 2019.

Vancouver:

Feher K. Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach. [Internet] [Doctoral dissertation]. University of Western Australia; 2010. [cited 2019 Mar 23]. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=29496&local_base=GEN01-INS01.

Council of Science Editors:

Feher K. Characterising the correlation structure of high dimensional genomic datasets using a random matrix theory approach. [Doctoral Dissertation]. University of Western Australia; 2010. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=29496&local_base=GEN01-INS01

12. Wang, Tengyao. Spectral methods and computational trade-offs in high-dimensional statistical inference.

Degree: PhD, 2016, University of Cambridge

 Spectral methods have become increasingly popular in designing fast algorithms for modern highdimensional datasets. This thesis looks at several problems in which spectral methods play… (more)

Subjects/Keywords: 519.5; Mathematical statistics; spectral methods; Davis-Kahan theorem; principal component analysis; PCA; restricted isometry; high-dimensional changepoint estimation; semi-definite programming

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APA (6th Edition):

Wang, T. (2016). Spectral methods and computational trade-offs in high-dimensional statistical inference. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/260825 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699320

Chicago Manual of Style (16th Edition):

Wang, Tengyao. “Spectral methods and computational trade-offs in high-dimensional statistical inference.” 2016. Doctoral Dissertation, University of Cambridge. Accessed March 23, 2019. https://www.repository.cam.ac.uk/handle/1810/260825 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699320.

MLA Handbook (7th Edition):

Wang, Tengyao. “Spectral methods and computational trade-offs in high-dimensional statistical inference.” 2016. Web. 23 Mar 2019.

Vancouver:

Wang T. Spectral methods and computational trade-offs in high-dimensional statistical inference. [Internet] [Doctoral dissertation]. University of Cambridge; 2016. [cited 2019 Mar 23]. Available from: https://www.repository.cam.ac.uk/handle/1810/260825 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699320.

Council of Science Editors:

Wang T. Spectral methods and computational trade-offs in high-dimensional statistical inference. [Doctoral Dissertation]. University of Cambridge; 2016. Available from: https://www.repository.cam.ac.uk/handle/1810/260825 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699320


University of Oulu

13. Kuismin, M. (Markku). On regularized estimation methods for precision and covariance matrix and statistical network inference.

Degree: 2018, University of Oulu

Abstract Estimation of the covariance matrix is an important problem in statistics in general because the covariance matrix is an essential part of principal component… (more)

Subjects/Keywords: LASSO; covariance matrix; graphical model; network estimation; precision matrix; ridge; Lasso; graafinen malli; kovarianssimatriisi; ridge; tarkkuusmatriisi; verkkojen estimointi; high-dimensional setting

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APA (6th Edition):

Kuismin, M. (. (2018). On regularized estimation methods for precision and covariance matrix and statistical network inference. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526220802

Chicago Manual of Style (16th Edition):

Kuismin, M (Markku). “On regularized estimation methods for precision and covariance matrix and statistical network inference.” 2018. Doctoral Dissertation, University of Oulu. Accessed March 23, 2019. http://urn.fi/urn:isbn:9789526220802.

MLA Handbook (7th Edition):

Kuismin, M (Markku). “On regularized estimation methods for precision and covariance matrix and statistical network inference.” 2018. Web. 23 Mar 2019.

Vancouver:

Kuismin M(. On regularized estimation methods for precision and covariance matrix and statistical network inference. [Internet] [Doctoral dissertation]. University of Oulu; 2018. [cited 2019 Mar 23]. Available from: http://urn.fi/urn:isbn:9789526220802.

Council of Science Editors:

Kuismin M(. On regularized estimation methods for precision and covariance matrix and statistical network inference. [Doctoral Dissertation]. University of Oulu; 2018. Available from: http://urn.fi/urn:isbn:9789526220802


University of Michigan

14. Firouzi, Hamed. High Dimensional Correlation Networks And Their Applications.

Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan

 Analysis of interactions between variables in a large data set has recently attracted special attention in the context of high dimensional multivariate statistical analysis. Variable… (more)

Subjects/Keywords: Big Data; High Dimensional Data; Correlation Analysis; Time Series Analysis; Covariance Estimation; Dimensionality Reduction; Electrical Engineering; Engineering

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APA (6th Edition):

Firouzi, H. (2015). High Dimensional Correlation Networks And Their Applications. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113492

Chicago Manual of Style (16th Edition):

Firouzi, Hamed. “High Dimensional Correlation Networks And Their Applications.” 2015. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/113492.

MLA Handbook (7th Edition):

Firouzi, Hamed. “High Dimensional Correlation Networks And Their Applications.” 2015. Web. 23 Mar 2019.

Vancouver:

Firouzi H. High Dimensional Correlation Networks And Their Applications. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/113492.

Council of Science Editors:

Firouzi H. High Dimensional Correlation Networks And Their Applications. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113492


EPFL

15. Leboucq, Alix. Meta-analysis of Incomplete Microarray Studies.

Degree: 2014, EPFL

 Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information, providing only… (more)

Subjects/Keywords: clustering; empirical Bayes estimation; hierarchical Bayesian model; high-dimensional data; large covariance matrix estimation; MCMC; meta-analysis; microarray gene expression data; modules

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APA (6th Edition):

Leboucq, A. (2014). Meta-analysis of Incomplete Microarray Studies. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/202163

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):

Leboucq, Alix. “Meta-analysis of Incomplete Microarray Studies.” 2014. Thesis, EPFL. Accessed March 23, 2019. http://infoscience.epfl.ch/record/202163.

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

MLA Handbook (7th Edition):

Leboucq, Alix. “Meta-analysis of Incomplete Microarray Studies.” 2014. Web. 23 Mar 2019.

Vancouver:

Leboucq A. Meta-analysis of Incomplete Microarray Studies. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Mar 23]. Available from: http://infoscience.epfl.ch/record/202163.

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

Council of Science Editors:

Leboucq A. Meta-analysis of Incomplete Microarray Studies. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/202163

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

16. Luu, Duy tung. Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes.

Degree: Docteur es, Mathematiques, 2017, Normandie

Dans plusieurs domaines des statistiques, y compris le traitement du signal et des images, l'estimation en grande dimension est une tâche importante pour recouvrer un… (more)

Subjects/Keywords: Estimation en grande dimension; A priori de faible complexité; Agrégation à poids exponentiels; Estimation pénalisée; Inégalité d'oracle; Diffusion de Langevin; Algorithme explicite-implicite; Consistence; High-dimensional estimation; Low-complexity prior; Exponential weighted aggregation; Penalized estimation; Oracle inequality; Langevin diffusion; Forward-backward algorithm; Consistency

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APA (6th Edition):

Luu, D. t. (2017). Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes. (Doctoral Dissertation). Normandie. Retrieved from http://www.theses.fr/2017NORMC234

Chicago Manual of Style (16th Edition):

Luu, Duy tung. “Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes.” 2017. Doctoral Dissertation, Normandie. Accessed March 23, 2019. http://www.theses.fr/2017NORMC234.

MLA Handbook (7th Edition):

Luu, Duy tung. “Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes.” 2017. Web. 23 Mar 2019.

Vancouver:

Luu Dt. Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes. [Internet] [Doctoral dissertation]. Normandie; 2017. [cited 2019 Mar 23]. Available from: http://www.theses.fr/2017NORMC234.

Council of Science Editors:

Luu Dt. Exponential weighted aggregation : oracle inequalities and algorithms : Agrégation à poids exponentiels : inégalités oracles et algorithmes. [Doctoral Dissertation]. Normandie; 2017. Available from: http://www.theses.fr/2017NORMC234


Case Western Reserve University

17. Liu, Peng. Adaptive Mixture Estimation and Subsampling PCA.

Degree: PhD, Sciences, 2009, Case Western Reserve University

 Data mining is important in scientific research, knowledge discovery and decision making. A typical challenge in data mining is that a data set may be… (more)

Subjects/Keywords: Statistics; large data; data mining; mixture models; Gaussian mixtures; parameter estimation; adaptive procedure; partial EM; high-dimensional data; large p small n; dimension reduction; feature selection; subsampling

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APA (6th Edition):

Liu, P. (2009). Adaptive Mixture Estimation and Subsampling PCA. (Doctoral Dissertation). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

Chicago Manual of Style (16th Edition):

Liu, Peng. “Adaptive Mixture Estimation and Subsampling PCA.” 2009. Doctoral Dissertation, Case Western Reserve University. Accessed March 23, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686.

MLA Handbook (7th Edition):

Liu, Peng. “Adaptive Mixture Estimation and Subsampling PCA.” 2009. Web. 23 Mar 2019.

Vancouver:

Liu P. Adaptive Mixture Estimation and Subsampling PCA. [Internet] [Doctoral dissertation]. Case Western Reserve University; 2009. [cited 2019 Mar 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686.

Council of Science Editors:

Liu P. Adaptive Mixture Estimation and Subsampling PCA. [Doctoral Dissertation]. Case Western Reserve University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686


University of Sydney

18. Liu, Xuan. Scalable Convex and Non-Convex Optimization for Dense Wireless Networks .

Degree: 2017, University of Sydney

 The evolution towards the next generation mobile networks is characterized by an unprecedented growth of smart devices. This will inevitably result in drastic data avalanches… (more)

Subjects/Keywords: Cloud-RANs; CSI; high-dimensional structured estimation; ADMM; spatial and temporal dynamics; massive device connectivity; Wireless sensor networks; energy efficiency; node clustering; data forecasting

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APA (6th Edition):

Liu, X. (2017). Scalable Convex and Non-Convex Optimization for Dense Wireless Networks . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17282

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):

Liu, Xuan. “Scalable Convex and Non-Convex Optimization for Dense Wireless Networks .” 2017. Thesis, University of Sydney. Accessed March 23, 2019. http://hdl.handle.net/2123/17282.

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

MLA Handbook (7th Edition):

Liu, Xuan. “Scalable Convex and Non-Convex Optimization for Dense Wireless Networks .” 2017. Web. 23 Mar 2019.

Vancouver:

Liu X. Scalable Convex and Non-Convex Optimization for Dense Wireless Networks . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2123/17282.

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

Council of Science Editors:

Liu X. Scalable Convex and Non-Convex Optimization for Dense Wireless Networks . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17282

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


University of Michigan

19. Shojaie, Ali. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.

Degree: PhD, Statistics, 2010, University of Michigan

 This dissertation discusses several aspects of estimation and inference for high dimensional networks, and is divided into three main parts. First, to assess the significance… (more)

Subjects/Keywords: High Dimensional Networks; Graphical Models; Biological Networks and Systems Biology; Small N Large P Asymptotics; Penalized Likelihood Estimation; Bioinformatics; Mathematics; Statistics and Numeric Data; Health Sciences; Science

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

APA (6th Edition):

Shojaie, A. (2010). Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77775

Chicago Manual of Style (16th Edition):

Shojaie, Ali. “Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.” 2010. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/77775.

MLA Handbook (7th Edition):

Shojaie, Ali. “Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems.” 2010. Web. 23 Mar 2019.

Vancouver:

Shojaie A. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/77775.

Council of Science Editors:

Shojaie A. Estimation and Inference in High Dimensional Networks, with Applications to Biological Systems. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77775


University of Cambridge

20. Wang, Tengyao. Spectral methods and computational trade-offs in high-dimensional statistical inference .

Degree: 2016, University of Cambridge

 Spectral methods have become increasingly popular in designing fast algorithms for modern highdimensional datasets. This thesis looks at several problems in which spectral methods play… (more)

Subjects/Keywords: Research Subject Categories::MATHEMATICS::Applied mathematics::Mathematical statistics; spectral methods; Davis-Kahan theorem; principal component analysis; PCA; restricted isometry; high-dimensional changepoint estimation; semi-definite programming

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APA (6th Edition):

Wang, T. (2016). Spectral methods and computational trade-offs in high-dimensional statistical inference . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/260825

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):

Wang, Tengyao. “Spectral methods and computational trade-offs in high-dimensional statistical inference .” 2016. Thesis, University of Cambridge. Accessed March 23, 2019. https://www.repository.cam.ac.uk/handle/1810/260825.

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

MLA Handbook (7th Edition):

Wang, Tengyao. “Spectral methods and computational trade-offs in high-dimensional statistical inference .” 2016. Web. 23 Mar 2019.

Vancouver:

Wang T. Spectral methods and computational trade-offs in high-dimensional statistical inference . [Internet] [Thesis]. University of Cambridge; 2016. [cited 2019 Mar 23]. Available from: https://www.repository.cam.ac.uk/handle/1810/260825.

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

Council of Science Editors:

Wang T. Spectral methods and computational trade-offs in high-dimensional statistical inference . [Thesis]. University of Cambridge; 2016. Available from: https://www.repository.cam.ac.uk/handle/1810/260825

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


ETH Zürich

21. Janková, Jana. Asymptotic Inference in Sparse High-dimensional Models.

Degree: 2017, ETH Zürich

High-dimensional data with a sparse structure occur in many areas of science, industry and entertainment. Diverse applications motivated the need to devise efficient statistical methods… (more)

Subjects/Keywords: Lasso; High-dimensional statistical inference; Sparsity; Asymptotic confidence intervals; Graphical model; Asymptotic normality; Covariance matrix estimation; Robust regression; Sparse principal component analysis; Asymptotic efficiency; De-biased Lasso

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

APA (6th Edition):

Janková, J. (2017). Asymptotic Inference in Sparse High-dimensional Models. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/248167

Chicago Manual of Style (16th Edition):

Janková, Jana. “Asymptotic Inference in Sparse High-dimensional Models.” 2017. Doctoral Dissertation, ETH Zürich. Accessed March 23, 2019. http://hdl.handle.net/20.500.11850/248167.

MLA Handbook (7th Edition):

Janková, Jana. “Asymptotic Inference in Sparse High-dimensional Models.” 2017. Web. 23 Mar 2019.

Vancouver:

Janková J. Asymptotic Inference in Sparse High-dimensional Models. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/20.500.11850/248167.

Council of Science Editors:

Janková J. Asymptotic Inference in Sparse High-dimensional Models. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/248167


University of Michigan

22. Shu, Hai. High Dimensional Dependent Data Analysis for Neuroimaging.

Degree: PhD, Biostatistics, 2016, University of Michigan

 This dissertation contains three projects focusing on two major high-dimensional problems for dependent data, particularly neuroimaging data: multiple testing and estimation of large covariance/precision matrices.… (more)

Subjects/Keywords: High dimensional dependent data; Neuroimaging; Multiple testing; Hidden Markov random field; Covariance/precision matrix estimation; Polynomial-decay-dominated temporal dependence; Statistics and Numeric Data; Science

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APA (6th Edition):

Shu, H. (2016). High Dimensional Dependent Data Analysis for Neuroimaging. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133198

Chicago Manual of Style (16th Edition):

Shu, Hai. “High Dimensional Dependent Data Analysis for Neuroimaging.” 2016. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/133198.

MLA Handbook (7th Edition):

Shu, Hai. “High Dimensional Dependent Data Analysis for Neuroimaging.” 2016. Web. 23 Mar 2019.

Vancouver:

Shu H. High Dimensional Dependent Data Analysis for Neuroimaging. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/133198.

Council of Science Editors:

Shu H. High Dimensional Dependent Data Analysis for Neuroimaging. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133198

23. Greenewald, Kristjan. High Dimensional Covariance Estimation for Spatio-Temporal Processes.

Degree: PhD, Electrical Engineering: Systems, 2017, University of Michigan

High dimensional time series and array-valued data are ubiquitous in signal processing, machine learning, and science. Due to the additional (temporal) direction, the total dimensionality… (more)

Subjects/Keywords: covariance estimation; nonstationary learning; low sample estimation; high dimensional data; metric learning; Electrical Engineering; Engineering

…increasing in interest recently, and statistical performance bounds for high dimensional estimation… …strong performance bounds for high-dimensional estimation of covariances under each model, and… …Dimensional Covariance Estimation for Spatio-Temporal Processes by Kristjan Greenewald Chairs… …Alfred O. Hero III and Shuheng Zhou High dimensional time series and array-valued data are… …covariance are useful tools to describe high dimensional distributions because (via the… 

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APA (6th Edition):

Greenewald, K. (2017). High Dimensional Covariance Estimation for Spatio-Temporal Processes. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/137082

Chicago Manual of Style (16th Edition):

Greenewald, Kristjan. “High Dimensional Covariance Estimation for Spatio-Temporal Processes.” 2017. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/137082.

MLA Handbook (7th Edition):

Greenewald, Kristjan. “High Dimensional Covariance Estimation for Spatio-Temporal Processes.” 2017. Web. 23 Mar 2019.

Vancouver:

Greenewald K. High Dimensional Covariance Estimation for Spatio-Temporal Processes. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/137082.

Council of Science Editors:

Greenewald K. High Dimensional Covariance Estimation for Spatio-Temporal Processes. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/137082

24. Robert, Sylvain. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.

Degree: 2017, ETH Zürich

 Data assimilation consists in estimating the state of a system, for example the atmosphere in numerical weather prediction (NWP), by combining information coming from the… (more)

Subjects/Keywords: ensemble Kalman filter; particle filter; high-dimensional filtering; DATA ASSIMILATION/NUMERICAL WEATHER PREDICTION (METEOROLOGY); CONVECTIVE PRECIPITATION SYSTEMS + THUNDERSTORMS, SHOWERS (METEOROLOGY); Weather forecast; Spatio-temporal data; STATISTICAL ANALYSIS AND INFERENCE METHODS (MATHEMATICAL STATISTICS); STATISTICAL COMPUTATION METHODS/METEOROLOGY; ESTIMATION OF PARAMETERS AND STATE ESTIMATION (MATHEMATICAL STATISTICS); KALMAN FILTERING (CONTROL SYSTEMS THEORY); STATE SPACE METHOD (CONTROL SYSTEMS THEORY)

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APA (6th Edition):

Robert, S. (2017). Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/184084

Chicago Manual of Style (16th Edition):

Robert, Sylvain. “Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.” 2017. Doctoral Dissertation, ETH Zürich. Accessed March 23, 2019. http://hdl.handle.net/20.500.11850/184084.

MLA Handbook (7th Edition):

Robert, Sylvain. “Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.” 2017. Web. 23 Mar 2019.

Vancouver:

Robert S. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/20.500.11850/184084.

Council of Science Editors:

Robert S. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/184084

25. Kolar, Mladen. Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems.

Degree: 2013, Carnegie Mellon University

 Extracting knowledge and providing insights into complex mechanisms underlying noisy high-dimensional data sets is of utmost importance in many scientific domains. Statistical modeling has become… (more)

Subjects/Keywords: Complex Systems; Dynamic Networks; Feature Selection; Gaussian Graphical Models; High-dimensional Inference; Markov Random Fields; Multi-task Learning; Semiparametric Estimation; Sparsity; Structure Learning; Undirected Graphical Models; Variable Screening; Varying Coefficient; Computer Sciences

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APA (6th Edition):

Kolar, M. (2013). Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/229

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):

Kolar, Mladen. “Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems.” 2013. Thesis, Carnegie Mellon University. Accessed March 23, 2019. http://repository.cmu.edu/dissertations/229.

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

MLA Handbook (7th Edition):

Kolar, Mladen. “Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems.” 2013. Web. 23 Mar 2019.

Vancouver:

Kolar M. Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2019 Mar 23]. Available from: http://repository.cmu.edu/dissertations/229.

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

Council of Science Editors:

Kolar M. Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/229

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

26. Van der Walt, Christiaan Maarten. Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt .

Degree: 2014, North-West University

 With the advent of the internet and advances in computing power, the collection of very large high-dimensional datasets has become feasible { understanding and modelling… (more)

Subjects/Keywords: Pattern recognition; Non-parametric density estimation; Kernel density estimation; Kernel bandwidth estimation; Maximum-likelihood; High-dimensional data; Artificial data; Probability density function

Estimation in High-dimensional Feature Spaces North-West University 2 Chapter One Introduction… …intended to perform density estimation in the high-dimensional features spaces often encountered… …was shown that non-parametric kernel density estimation can be performed in high-dimensional… …The MLL ML Kernel Density Estimation in High-dimensional Feature Spaces North-West… …concluding remarks and suggest future work. ML Kernel Density Estimation in High-dimensional… 

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APA (6th Edition):

Van der Walt, C. M. (2014). Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt . (Thesis). North-West University. Retrieved from http://hdl.handle.net/10394/10635

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):

Van der Walt, Christiaan Maarten. “Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt .” 2014. Thesis, North-West University. Accessed March 23, 2019. http://hdl.handle.net/10394/10635.

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

MLA Handbook (7th Edition):

Van der Walt, Christiaan Maarten. “Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt .” 2014. Web. 23 Mar 2019.

Vancouver:

Van der Walt CM. Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt . [Internet] [Thesis]. North-West University; 2014. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/10394/10635.

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

Council of Science Editors:

Van der Walt CM. Maximum-likelihood kernel density estimation in high-dimensional feature spaces /| C.M. van der Walt . [Thesis]. North-West University; 2014. Available from: http://hdl.handle.net/10394/10635

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


Queensland University of Technology

27. Wu, Burton. New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry.

Degree: 2011, Queensland University of Technology

 This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’… (more)

Subjects/Keywords: Gaussian mixture model (GMM), mixture models, probability density estimation, variational bayes (VB), Bayesian statistics, data mining (DM), combinational data analysis (CDA), profiling, segmentation, clustering, feature extraction; behavioural characteristics, consumer behaviour, customer behaviour, consumption behaviour, customer relationship management (CRM), relationship marketing (RM), human mobility pattern, spatial behaviour, temporal behaviour, circular data, data stream; high dimensional data, call detail records (CDR), wireless telecommunication industry

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APA (6th Edition):

Wu, B. (2011). New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/46084/

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):

Wu, Burton. “New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry.” 2011. Thesis, Queensland University of Technology. Accessed March 23, 2019. https://eprints.qut.edu.au/46084/.

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

MLA Handbook (7th Edition):

Wu, Burton. “New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry.” 2011. Web. 23 Mar 2019.

Vancouver:

Wu B. New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry. [Internet] [Thesis]. Queensland University of Technology; 2011. [cited 2019 Mar 23]. Available from: https://eprints.qut.edu.au/46084/.

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

Council of Science Editors:

Wu B. New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry. [Thesis]. Queensland University of Technology; 2011. Available from: https://eprints.qut.edu.au/46084/

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

28. Chen, Yilun. Regularized Estimation of High-dimensional Covariance Matrices.

Degree: PhD, Electrical Engineering: Systems, 2011, University of Michigan

 Many signal processing methods are fundamentally related to the estimation of covariance matrices. In cases where there are a large number of covariates the dimension… (more)

Subjects/Keywords: High-dimensional; Covariance Matrix Estimation; Compressive Sensing; Recursive Group Lasso; Sparse Least-Mean-Square; Analog-to-Digital Converter; Electrical Engineering; Engineering

…143 xii ABSTRACT Regularized Estimation of High-dimensional Covariance Matrices by Yilun… …develop necessary components for covariance estimation in the high-dimensional setting. The… …dissertation makes contributions in two main areas of covariance estimation: (1) high… …dimensional shrinkage regularized covariance estimation and (2) recursive online… …sparse structures of the high-dimensional covariance matrix from a set of random projections… 

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

APA (6th Edition):

Chen, Y. (2011). Regularized Estimation of High-dimensional Covariance Matrices. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/86396

Chicago Manual of Style (16th Edition):

Chen, Yilun. “Regularized Estimation of High-dimensional Covariance Matrices.” 2011. Doctoral Dissertation, University of Michigan. Accessed March 23, 2019. http://hdl.handle.net/2027.42/86396.

MLA Handbook (7th Edition):

Chen, Yilun. “Regularized Estimation of High-dimensional Covariance Matrices.” 2011. Web. 23 Mar 2019.

Vancouver:

Chen Y. Regularized Estimation of High-dimensional Covariance Matrices. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2019 Mar 23]. Available from: http://hdl.handle.net/2027.42/86396.

Council of Science Editors:

Chen Y. Regularized Estimation of High-dimensional Covariance Matrices. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/86396


Université de Bordeaux I

29. Ayvazyan, Vigen. Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing.

Degree: Docteur es, Mécanique et énergétique, 2012, Université de Bordeaux I

La thermographie infrarouge est une méthode largement employée pour la caractérisation des propriétés thermophysiques des matériaux. L’avènement des diodes laser pratiques, peu onéreuses et aux… (more)

Subjects/Keywords: Thermographie infrarouge; Contrôle non destructif CND; Techniques inverses; Caractérisation thermique; Décomposition en valeurs singulières SVD; Double décomposition en valeurs singulières 2SVD; Analyse en composantes principales PCA; Développement en valeurs singulières SVE; Estimation de paramètres thermophysiques; Profils de diffusivités thermiques longitudinales; Estimation de champs de température initiaux; Diffusion thermique tridimensionnelle; Méthode flash; Flash face avant; Diodes laser; Point source impulsionnel; Méthodes modales; Analyse de corrélations; Compression de données; Transformations orthogonales du signal; Champs de température séparables; Traitement d'une grande quantité de données; Séparabilité spatiale; Transformées de Fourier; Filtrage de données; Matériaux hétérogènes; Petites échelles; Matériaux composites; Infrared thermography; Non destructive testing NDT; Non destructive evaluation NDE; Inverse techniques; Thermal characterization; Singular value decomposition SVD; Double singular value decomposition 2SVD; Principal component analysis PCA; Singular value expansion SVE; Estimation of thermophysical properties; Longitudinal thermal diffusivity profiles; Estimation of initial temperature fields; Three-dimensional heat diffusion; Flash method; Laser diodes; Instantaneous point source of heat; Correlation analysis; Data compression; Orthogonal transforms of the signal; Separable temperature fields; High volume data processing; Fourier transforms; Data filtering; Heterogeneous materials; Small scales; Composite materials

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APA (6th Edition):

Ayvazyan, V. (2012). Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing. (Doctoral Dissertation). Université de Bordeaux I. Retrieved from http://www.theses.fr/2012BOR14671

Chicago Manual of Style (16th Edition):

Ayvazyan, Vigen. “Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing.” 2012. Doctoral Dissertation, Université de Bordeaux I. Accessed March 23, 2019. http://www.theses.fr/2012BOR14671.

MLA Handbook (7th Edition):

Ayvazyan, Vigen. “Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing.” 2012. Web. 23 Mar 2019.

Vancouver:

Ayvazyan V. Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing. [Internet] [Doctoral dissertation]. Université de Bordeaux I; 2012. [cited 2019 Mar 23]. Available from: http://www.theses.fr/2012BOR14671.

Council of Science Editors:

Ayvazyan V. Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif : Study of separable temperatur fields with a double singular value decomposition : some applications in characterization of thermophysical properties of materials and non destructive testing. [Doctoral Dissertation]. Université de Bordeaux I; 2012. Available from: http://www.theses.fr/2012BOR14671

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