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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

URL: http://hdl.handle.net/11299/198991

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2152/43772

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

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

URL: http://arks.princeton.edu/ark:/88435/dsp01ht24wn13d

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://jhir.library.jhu.edu/handle/1774.2/37867

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.escholarship.org/uc/item/9vw1524p

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/1773/37084

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/20.500.11850/197854

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.theses.fr/2016SACLS245

►

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2027.42/110499

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1969.1/3853

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=29496&local_base=GEN01-INS01

►

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://www.repository.cam.ac.uk/handle/1810/260825 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699320

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://urn.fi/urn:isbn:9789526220802

►

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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2027.42/113492

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://infoscience.epfl.ch/record/202163

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.theses.fr/2017NORMC234

►

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2123/17282

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

University of Michigan

19.
Shojaie, Ali.
* Estimation* and Inference in

Degree: PhD, Statistics, 2010, University of Michigan

URL: http://hdl.handle.net/2027.42/77775

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://www.repository.cam.ac.uk/handle/1810/260825

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

ETH Zürich

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

Degree: 2017, ETH Zürich

URL: http://hdl.handle.net/20.500.11850/248167

► *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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2027.42/133198

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2027.42/137082

► *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…

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/20.500.11850/184084

► 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)

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://repository.cmu.edu/dissertations/229

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10394/10635

► 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*…

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://eprints.qut.edu.au/46084/

► 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

Record Details Similar Records

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

APA (6^{th} 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/

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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/.

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/

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

URL: http://hdl.handle.net/2027.42/86396

► 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…

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.theses.fr/2012BOR14671

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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