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You searched for subject:(High dimensional). Showing records 1 – 30 of 572 total matches.

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1. Freyaldenhoven, Simon. Essays on Factor Models and Latent Variables in Economics.

Degree: Department of Economics, 2018, Brown University

 This dissertation examines the modeling of latent variables in economics in a variety of settings. The first two chapters contribute to the growing body of… (more)

Subjects/Keywords: high dimensional data

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APA (6th Edition):

Freyaldenhoven, S. (2018). Essays on Factor Models and Latent Variables in Economics. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792643/

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):

Freyaldenhoven, Simon. “Essays on Factor Models and Latent Variables in Economics.” 2018. Thesis, Brown University. Accessed February 28, 2021. https://repository.library.brown.edu/studio/item/bdr:792643/.

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

MLA Handbook (7th Edition):

Freyaldenhoven, Simon. “Essays on Factor Models and Latent Variables in Economics.” 2018. Web. 28 Feb 2021.

Vancouver:

Freyaldenhoven S. Essays on Factor Models and Latent Variables in Economics. [Internet] [Thesis]. Brown University; 2018. [cited 2021 Feb 28]. Available from: https://repository.library.brown.edu/studio/item/bdr:792643/.

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

Council of Science Editors:

Freyaldenhoven S. Essays on Factor Models and Latent Variables in Economics. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792643/

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


University of Illinois – Urbana-Champaign

2. Wang, Runmin. Statistical inference for high-dimensional data via U-statistcs.

Degree: PhD, Statistics, 2020, University of Illinois – Urbana-Champaign

 Owing to the advances in the science and technology, there is a surge of interest in high-dimensional data. Many methods developed in low or fixed… (more)

Subjects/Keywords: High-dimensional data; U-statistics

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APA (6th Edition):

Wang, R. (2020). Statistical inference for high-dimensional data via U-statistcs. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/108476

Chicago Manual of Style (16th Edition):

Wang, Runmin. “Statistical inference for high-dimensional data via U-statistcs.” 2020. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 28, 2021. http://hdl.handle.net/2142/108476.

MLA Handbook (7th Edition):

Wang, Runmin. “Statistical inference for high-dimensional data via U-statistcs.” 2020. Web. 28 Feb 2021.

Vancouver:

Wang R. Statistical inference for high-dimensional data via U-statistcs. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/2142/108476.

Council of Science Editors:

Wang R. Statistical inference for high-dimensional data via U-statistcs. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/108476


University of New South Wales

3. Gilbert, Alexander. Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation.

Degree: Mathematics & Statistics, 2018, University of New South Wales

 Approximating high and infinite dimensional integrals numerically is in general a very difficult problem. However, it is also one that arises in several applications from… (more)

Subjects/Keywords: High-dimensional integration; Numerical integration; Quasi-Monte Carlo; Infinite-dimensional integration

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APA (6th Edition):

Gilbert, A. (2018). Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60171 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51009/SOURCE2?view=true

Chicago Manual of Style (16th Edition):

Gilbert, Alexander. “Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation.” 2018. Doctoral Dissertation, University of New South Wales. Accessed February 28, 2021. http://handle.unsw.edu.au/1959.4/60171 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51009/SOURCE2?view=true.

MLA Handbook (7th Edition):

Gilbert, Alexander. “Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation.” 2018. Web. 28 Feb 2021.

Vancouver:

Gilbert A. Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Feb 28]. Available from: http://handle.unsw.edu.au/1959.4/60171 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51009/SOURCE2?view=true.

Council of Science Editors:

Gilbert A. Algorithms for numerical integration in high and infinite dimensions: Analysis, applications and implementation. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60171 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51009/SOURCE2?view=true


University of Alberta

4. Fedoruk, John P. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.

Degree: MS, Department of Mathematical and Statistical Sciences, 2016, University of Alberta

 In an increasingly data-driven society, there is a growing need to simplify high-dimensional data sets. Over the course of the past three decades, the Johnson… (more)

Subjects/Keywords: Dimensionality Reduction; High Dimensional Data; Johnson Lindenstrauss

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APA (6th Edition):

Fedoruk, J. P. (2016). Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cm039k5065

Chicago Manual of Style (16th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Masters Thesis, University of Alberta. Accessed February 28, 2021. https://era.library.ualberta.ca/files/cm039k5065.

MLA Handbook (7th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Web. 28 Feb 2021.

Vancouver:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2021 Feb 28]. Available from: https://era.library.ualberta.ca/files/cm039k5065.

Council of Science Editors:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cm039k5065


University of Michigan

5. Qian, Cheng. Some Advances on Modeling High-Dimensional Data with Complex Structures.

Degree: PhD, Statistics, 2017, University of Michigan

 Recent advances in technology have created an abundance of high-dimensional data and made its analysis possible. These data require new, computationally efficient methodology and new… (more)

Subjects/Keywords: High-Dimensional; Statistics and Numeric Data; Science

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APA (6th Edition):

Qian, C. (2017). Some Advances on Modeling High-Dimensional Data with Complex Structures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140828

Chicago Manual of Style (16th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Doctoral Dissertation, University of Michigan. Accessed February 28, 2021. http://hdl.handle.net/2027.42/140828.

MLA Handbook (7th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Web. 28 Feb 2021.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/2027.42/140828.

Council of Science Editors:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140828


Cornell University

6. Gaynanova, Irina. Estimation Of Sparse Low-Dimensional Linear Projections.

Degree: PhD, Statistics, 2015, Cornell University

 Many multivariate analysis problems are unified under the framework of linear projections. These projections can be tailored towards the analysis of variance (principal components), classification… (more)

Subjects/Keywords: multivariate analysis; high-dimensional statistics; classification

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APA (6th Edition):

Gaynanova, I. (2015). Estimation Of Sparse Low-Dimensional Linear Projections. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/40643

Chicago Manual of Style (16th Edition):

Gaynanova, Irina. “Estimation Of Sparse Low-Dimensional Linear Projections.” 2015. Doctoral Dissertation, Cornell University. Accessed February 28, 2021. http://hdl.handle.net/1813/40643.

MLA Handbook (7th Edition):

Gaynanova, Irina. “Estimation Of Sparse Low-Dimensional Linear Projections.” 2015. Web. 28 Feb 2021.

Vancouver:

Gaynanova I. Estimation Of Sparse Low-Dimensional Linear Projections. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/1813/40643.

Council of Science Editors:

Gaynanova I. Estimation Of Sparse Low-Dimensional Linear Projections. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/40643


Penn State University

7. Yu, Ye. A New Variable Screening Procedure for COX'S Model.

Degree: 2014, Penn State University

 Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other �fields. In this thesis, we propose a… (more)

Subjects/Keywords: screening; COX's model; high dimensional; iterative procedure

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APA (6th Edition):

Yu, Y. (2014). A New Variable Screening Procedure for COX'S Model. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/23542

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):

Yu, Ye. “A New Variable Screening Procedure for COX'S Model.” 2014. Thesis, Penn State University. Accessed February 28, 2021. https://submit-etda.libraries.psu.edu/catalog/23542.

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

MLA Handbook (7th Edition):

Yu, Ye. “A New Variable Screening Procedure for COX'S Model.” 2014. Web. 28 Feb 2021.

Vancouver:

Yu Y. A New Variable Screening Procedure for COX'S Model. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Feb 28]. Available from: https://submit-etda.libraries.psu.edu/catalog/23542.

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

Council of Science Editors:

Yu Y. A New Variable Screening Procedure for COX'S Model. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/23542

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

8. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

 Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data… (more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

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

APA (6th Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/298064

Chicago Manual of Style (16th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed February 28, 2021. https://www.repository.cam.ac.uk/handle/1810/298064.

MLA Handbook (7th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 28 Feb 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Feb 28]. Available from: https://www.repository.cam.ac.uk/handle/1810/298064.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/298064


Delft University of Technology

9. Grisel, Bastiaan (author). The analysis of three-dimensional embeddings in Virtual Reality.

Degree: 2018, Delft University of Technology

Dimensionality reduction algorithms transform high-dimensional datasets with many attributes per observation into lower-dimensional representations (called embeddings) such that the structure of the dataset is maintained… (more)

Subjects/Keywords: virtual; reality; embedding; visualisation; data; high-dimensional

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APA (6th Edition):

Grisel, B. (. (2018). The analysis of three-dimensional embeddings in Virtual Reality. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f

Chicago Manual of Style (16th Edition):

Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Masters Thesis, Delft University of Technology. Accessed February 28, 2021. http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.

MLA Handbook (7th Edition):

Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Web. 28 Feb 2021.

Vancouver:

Grisel B(. The analysis of three-dimensional embeddings in Virtual Reality. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 28]. Available from: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.

Council of Science Editors:

Grisel B(. The analysis of three-dimensional embeddings in Virtual Reality. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f


University of Minnesota

10. Zhu, Yunzhang. Grouping penalties and its applications to high-dimensional models.

Degree: PhD, Statistics, 2014, University of Minnesota

 Part I: In high-dimensional regression, grouping pursuit and feature selection have their own merits while complementing each other in battling the curse of dimensionality. To… (more)

Subjects/Keywords: Graphical models; Grouping penalty; High-dimensional statistics

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APA (6th Edition):

Zhu, Y. (2014). Grouping penalties and its applications to high-dimensional models. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/165147

Chicago Manual of Style (16th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Doctoral Dissertation, University of Minnesota. Accessed February 28, 2021. http://hdl.handle.net/11299/165147.

MLA Handbook (7th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Web. 28 Feb 2021.

Vancouver:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/11299/165147.

Council of Science Editors:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/165147


University of Minnesota

11. Ye, Changqing. Network selection, information filtering and scalable computation.

Degree: PhD, Statistics, 2014, University of Minnesota

 This dissertation explores two application scenarios of sparsity pursuit method on large scale data sets. The first scenario is classification and regression in analyzing high(more)

Subjects/Keywords: High dimensional data; Machine learning; Recommendation; Statistics

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APA (6th Edition):

Ye, C. (2014). Network selection, information filtering and scalable computation. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/172631

Chicago Manual of Style (16th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Doctoral Dissertation, University of Minnesota. Accessed February 28, 2021. http://hdl.handle.net/11299/172631.

MLA Handbook (7th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Web. 28 Feb 2021.

Vancouver:

Ye C. Network selection, information filtering and scalable computation. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/11299/172631.

Council of Science Editors:

Ye C. Network selection, information filtering and scalable computation. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/172631


University of Minnesota

12. 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 (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 February 28, 2021. http://hdl.handle.net/11299/198991.

MLA Handbook (7th Edition):

Chen, Sheng. “Computational and Statistical Aspects of High-Dimensional Structured Estimation.” 2018. Web. 28 Feb 2021.

Vancouver:

Chen S. Computational and Statistical Aspects of High-Dimensional Structured Estimation. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Feb 28]. 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


Massey University

13. Ullah, Insha. Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .

Degree: 2015, Massey University

High-dimensional data sets, particularly those where the number of variables exceeds the number of observations, are now common in many subject areas including genetics, ecology,… (more)

Subjects/Keywords: Multivariate analysis; High-dimensional data; Covariance

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APA (6th Edition):

Ullah, I. (2015). Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand . (Thesis). Massey University. Retrieved from http://hdl.handle.net/10179/6608

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):

Ullah, Insha. “Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .” 2015. Thesis, Massey University. Accessed February 28, 2021. http://hdl.handle.net/10179/6608.

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

MLA Handbook (7th Edition):

Ullah, Insha. “Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .” 2015. Web. 28 Feb 2021.

Vancouver:

Ullah I. Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand . [Internet] [Thesis]. Massey University; 2015. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10179/6608.

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

Council of Science Editors:

Ullah I. Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand . [Thesis]. Massey University; 2015. Available from: http://hdl.handle.net/10179/6608

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


University of Cambridge

14. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

 Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data… (more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

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

APA (6th Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

Chicago Manual of Style (16th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed February 28, 2021. https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

MLA Handbook (7th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 28 Feb 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Feb 28]. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044


Vanderbilt University

15. -3545-4710. Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics.

Degree: PhD, Economics, 2020, Vanderbilt University

 The new generation machine learning and high-dimensional techniques have become powerful tools for economists. In economics, researchers are often facing cross-sectional dependence. However, the existing… (more)

Subjects/Keywords: cluster robust inference; high-dimensional; machine learning

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APA (6th Edition):

-3545-4710. (2020). Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15939

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

Chicago Manual of Style (16th Edition):

-3545-4710. “Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics.” 2020. Doctoral Dissertation, Vanderbilt University. Accessed February 28, 2021. http://hdl.handle.net/1803/15939.

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

MLA Handbook (7th Edition):

-3545-4710. “Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics.” 2020. Web. 28 Feb 2021.

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

Vancouver:

-3545-4710. Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics. [Internet] [Doctoral dissertation]. Vanderbilt University; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/1803/15939.

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

Council of Science Editors:

-3545-4710. Three Essays in Cluster Robust Machine Learning and High-Dimensional Econometrics. [Doctoral Dissertation]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/15939

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


Virginia Tech

16. Blake, Patrick Michael. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.

Degree: MS, Electrical Engineering, 2019, Virginia Tech

 Many data sets have too many features for conventional pattern recognition techniques to work properly. This thesis investigates techniques that alleviate these difficulties. One such… (more)

Subjects/Keywords: high-dimensional data; biclustering; VISDA; VISDApy

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APA (6th Edition):

Blake, P. M. (2019). Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87392

Chicago Manual of Style (16th Edition):

Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Masters Thesis, Virginia Tech. Accessed February 28, 2021. http://hdl.handle.net/10919/87392.

MLA Handbook (7th Edition):

Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Web. 28 Feb 2021.

Vancouver:

Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10919/87392.

Council of Science Editors:

Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87392


Princeton University

17. Li, Yan. Optimal Learning in High Dimensions .

Degree: PhD, 2016, Princeton University

 Collecting information in the course of sequential decision-making can be extremely challenging in high-dimensional settings, where the number of measurement budget is much smaller than… (more)

Subjects/Keywords: Bayesian Optimization; High-dimensional Statistics; Optimal Learning

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APA (6th Edition):

Li, Y. (2016). Optimal Learning in High Dimensions . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp014m90dx99b

Chicago Manual of Style (16th Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Doctoral Dissertation, Princeton University. Accessed February 28, 2021. http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

MLA Handbook (7th Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Web. 28 Feb 2021.

Vancouver:

Li Y. Optimal Learning in High Dimensions . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2021 Feb 28]. Available from: http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

Council of Science Editors:

Li Y. Optimal Learning in High Dimensions . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp014m90dx99b


University of Minnesota

18. Sivakumar, Vidyashankar. Beyond Sub-Gaussian and Independent Data in High Dimensional Regression.

Degree: PhD, Computer Science, 2020, University of Minnesota

 The past three decades has seen major developments in high-dimensional regression models leading to their successful use in applications from multiple domains including climate science,… (more)

Subjects/Keywords: Bandits and online learning; High-dimensional regression

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APA (6th Edition):

Sivakumar, V. (2020). Beyond Sub-Gaussian and Independent Data in High Dimensional Regression. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/217800

Chicago Manual of Style (16th Edition):

Sivakumar, Vidyashankar. “Beyond Sub-Gaussian and Independent Data in High Dimensional Regression.” 2020. Doctoral Dissertation, University of Minnesota. Accessed February 28, 2021. http://hdl.handle.net/11299/217800.

MLA Handbook (7th Edition):

Sivakumar, Vidyashankar. “Beyond Sub-Gaussian and Independent Data in High Dimensional Regression.” 2020. Web. 28 Feb 2021.

Vancouver:

Sivakumar V. Beyond Sub-Gaussian and Independent Data in High Dimensional Regression. [Internet] [Doctoral dissertation]. University of Minnesota; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/11299/217800.

Council of Science Editors:

Sivakumar V. Beyond Sub-Gaussian and Independent Data in High Dimensional Regression. [Doctoral Dissertation]. University of Minnesota; 2020. Available from: http://hdl.handle.net/11299/217800


University of Waterloo

19. Alev, Vedat Levi. Higher Order Random Walks, Local Spectral Expansion, and Applications.

Degree: 2020, University of Waterloo

 The study of spectral expansion of graphs and expander graphs has been an extremely fruitful line of research in Mathematics and Computer Science, with applications… (more)

Subjects/Keywords: spectral gap; Markov chains; high dimensional expanders; high dimensional expansion; random sampling

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APA (6th Edition):

Alev, V. L. (2020). Higher Order Random Walks, Local Spectral Expansion, and Applications. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16310

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):

Alev, Vedat Levi. “Higher Order Random Walks, Local Spectral Expansion, and Applications.” 2020. Thesis, University of Waterloo. Accessed February 28, 2021. http://hdl.handle.net/10012/16310.

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

MLA Handbook (7th Edition):

Alev, Vedat Levi. “Higher Order Random Walks, Local Spectral Expansion, and Applications.” 2020. Web. 28 Feb 2021.

Vancouver:

Alev VL. Higher Order Random Walks, Local Spectral Expansion, and Applications. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/10012/16310.

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

Council of Science Editors:

Alev VL. Higher Order Random Walks, Local Spectral Expansion, and Applications. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16310

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


NSYSU

20. Tai, Chiech-an. An Automatic Data Clustering Algorithm based on Differential Evolution.

Degree: Master, Computer Science and Engineering, 2013, NSYSU

 As one of the traditional optimization problems, clustering still plays a vital role for the re-searches both theoretically and practically nowadays. Although many successful clustering… (more)

Subjects/Keywords: automatic clustering; data clustering; high-dimensional dataset; histogram analysis; differential evolution

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APA (6th Edition):

Tai, C. (2013). An Automatic Data Clustering Algorithm based on Differential Evolution. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

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):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Thesis, NSYSU. Accessed February 28, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

MLA Handbook (7th Edition):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Web. 28 Feb 2021.

Vancouver:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Internet] [Thesis]. NSYSU; 2013. [cited 2021 Feb 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

Council of Science Editors:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

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


NSYSU

21. Wang, Kai-hsuan. Optical study of monolayer MoS2 film in high magnetic field.

Degree: Master, Physics, 2015, NSYSU

 Recently, motivated by the discovery of graphene, two-dimensional materials have attracted more attention. MoS2 is one of focused two-dimensional material [1,2], owning two its gapped… (more)

Subjects/Keywords: band-gap; MoS2; absorption spectrum; high magnetic field; Two-dimensional materials

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APA (6th Edition):

Wang, K. (2015). Optical study of monolayer MoS2 film in high magnetic field. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628115-091340

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, Kai-hsuan. “Optical study of monolayer MoS2 film in high magnetic field.” 2015. Thesis, NSYSU. Accessed February 28, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628115-091340.

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

MLA Handbook (7th Edition):

Wang, Kai-hsuan. “Optical study of monolayer MoS2 film in high magnetic field.” 2015. Web. 28 Feb 2021.

Vancouver:

Wang K. Optical study of monolayer MoS2 film in high magnetic field. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Feb 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628115-091340.

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

Council of Science Editors:

Wang K. Optical study of monolayer MoS2 film in high magnetic field. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628115-091340

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


University of California – Riverside

22. Zakaria, Jesin. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.

Degree: Computer Science, 2013, University of California – Riverside

 Data mining and knowledge discovery has attracted a great deal of attention in information technology in recent years. The rapid progress of computer hardware technology… (more)

Subjects/Keywords: Computer science; Clustering; Data Mining; High Dimensional Data; Scalable; Time Series

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APA (6th Edition):

Zakaria, J. (2013). Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/660316zp

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):

Zakaria, Jesin. “Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.” 2013. Thesis, University of California – Riverside. Accessed February 28, 2021. http://www.escholarship.org/uc/item/660316zp.

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

MLA Handbook (7th Edition):

Zakaria, Jesin. “Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.” 2013. Web. 28 Feb 2021.

Vancouver:

Zakaria J. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. [Internet] [Thesis]. University of California – Riverside; 2013. [cited 2021 Feb 28]. Available from: http://www.escholarship.org/uc/item/660316zp.

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

Council of Science Editors:

Zakaria J. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. [Thesis]. University of California – Riverside; 2013. Available from: http://www.escholarship.org/uc/item/660316zp

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


University of California – Berkeley

23. Bhattacharyya, Sharmodeep. A Study of High-dimensional Clustering and Statistical Inference on Networks.

Degree: Statistics, 2013, University of California – Berkeley

 Clustering is an important unsupervised classification technique. In supervised classification, we are provided with a collection of labeled (pre-classified) patterns and the problem is to… (more)

Subjects/Keywords: Statistics; Bootstrap; Clustering; Community detection; Elliptical distributions; High-dimensional inference; Networks

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APA (6th Edition):

Bhattacharyya, S. (2013). A Study of High-dimensional Clustering and Statistical Inference on Networks. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9sx0k48k

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):

Bhattacharyya, Sharmodeep. “A Study of High-dimensional Clustering and Statistical Inference on Networks.” 2013. Thesis, University of California – Berkeley. Accessed February 28, 2021. http://www.escholarship.org/uc/item/9sx0k48k.

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

MLA Handbook (7th Edition):

Bhattacharyya, Sharmodeep. “A Study of High-dimensional Clustering and Statistical Inference on Networks.” 2013. Web. 28 Feb 2021.

Vancouver:

Bhattacharyya S. A Study of High-dimensional Clustering and Statistical Inference on Networks. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2021 Feb 28]. Available from: http://www.escholarship.org/uc/item/9sx0k48k.

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

Council of Science Editors:

Bhattacharyya S. A Study of High-dimensional Clustering and Statistical Inference on Networks. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/9sx0k48k

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


Tulane University

24. Qu, Zhe. High-dimensional statistical data integration.

Degree: 2019, Tulane University

[email protected]

Modern biomedical studies often collect multiple types of high-dimensional data on a common set of objects. A representative model for the integrative analysis of… (more)

Subjects/Keywords: High-dimensional data analysis; Data integration; Canonical correlation analysis

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APA (6th Edition):

Qu, Z. (2019). High-dimensional statistical data integration. (Thesis). Tulane University. Retrieved from https://digitallibrary.tulane.edu/islandora/object/tulane:106916

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):

Qu, Zhe. “High-dimensional statistical data integration.” 2019. Thesis, Tulane University. Accessed February 28, 2021. https://digitallibrary.tulane.edu/islandora/object/tulane:106916.

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

MLA Handbook (7th Edition):

Qu, Zhe. “High-dimensional statistical data integration.” 2019. Web. 28 Feb 2021.

Vancouver:

Qu Z. High-dimensional statistical data integration. [Internet] [Thesis]. Tulane University; 2019. [cited 2021 Feb 28]. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:106916.

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

Council of Science Editors:

Qu Z. High-dimensional statistical data integration. [Thesis]. Tulane University; 2019. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:106916

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


Tulane University

25. Xu, Chao. Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

Degree: 2018, Tulane University

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in… (more)

Subjects/Keywords: extreme sampling; high-dimensional regression; genetic data analysis

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APA (6th Edition):

Xu, C. (2018). Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits. (Thesis). Tulane University. Retrieved from https://digitallibrary.tulane.edu/islandora/object/tulane:78817

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

Chicago Manual of Style (16th Edition):

Xu, Chao. “Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.” 2018. Thesis, Tulane University. Accessed February 28, 2021. https://digitallibrary.tulane.edu/islandora/object/tulane:78817.

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

MLA Handbook (7th Edition):

Xu, Chao. “Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.” 2018. Web. 28 Feb 2021.

Vancouver:

Xu C. Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits. [Internet] [Thesis]. Tulane University; 2018. [cited 2021 Feb 28]. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:78817.

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

Council of Science Editors:

Xu C. Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits. [Thesis]. Tulane University; 2018. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:78817

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

26. Hwang, Sung Jin. Geometric Representations of High Dimensional Random Data.

Degree: PhD, Electrical Engineering-Systems, 2012, University of Michigan

 This thesis introduces geometric representations relevant to the analysis of datasets of random vectors in high dimension. These representations are used to study the behavior… (more)

Subjects/Keywords: High Dimensional Data; Engineering

…foundation to analyze and understand the practice. When random data from a high dimensional space… …representations for high-dimensional data are based on linear models. For example, principal component… …and Alfred O. Hero III (2012). “Shortest path for high-dimensional data… …high. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A run through the family… …z; R2 ). Since ∣ξ i − ξ j ∣ ≤ 2−1 R2 , with high probability we have L n (ξ i… 

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APA (6th Edition):

Hwang, S. J. (2012). Geometric Representations of High Dimensional Random Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/96097

Chicago Manual of Style (16th Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Doctoral Dissertation, University of Michigan. Accessed February 28, 2021. http://hdl.handle.net/2027.42/96097.

MLA Handbook (7th Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Web. 28 Feb 2021.

Vancouver:

Hwang SJ. Geometric Representations of High Dimensional Random Data. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/2027.42/96097.

Council of Science Editors:

Hwang SJ. Geometric Representations of High Dimensional Random Data. [Doctoral Dissertation]. University of Michigan; 2012. Available from: http://hdl.handle.net/2027.42/96097


Georgia Tech

27. Ahlin, Konrad Jeffrey. The secant and traveling artificial potential field approaches to high dimensional robotic path planning.

Degree: PhD, Mechanical Engineering, 2018, Georgia Tech

 The field of robotic path planning is rich and diverse. As more complicated systems have become automated, the need for simple methods that can navigate… (more)

Subjects/Keywords: Artificial; Potential; Field; Secant; Robotic; Path planning; Trajectory; Dynamic; High dimensional

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APA (6th Edition):

Ahlin, K. J. (2018). The secant and traveling artificial potential field approaches to high dimensional robotic path planning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62196

Chicago Manual of Style (16th Edition):

Ahlin, Konrad Jeffrey. “The secant and traveling artificial potential field approaches to high dimensional robotic path planning.” 2018. Doctoral Dissertation, Georgia Tech. Accessed February 28, 2021. http://hdl.handle.net/1853/62196.

MLA Handbook (7th Edition):

Ahlin, Konrad Jeffrey. “The secant and traveling artificial potential field approaches to high dimensional robotic path planning.” 2018. Web. 28 Feb 2021.

Vancouver:

Ahlin KJ. The secant and traveling artificial potential field approaches to high dimensional robotic path planning. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/1853/62196.

Council of Science Editors:

Ahlin KJ. The secant and traveling artificial potential field approaches to high dimensional robotic path planning. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/62196


University of Illinois – Urbana-Champaign

28. Ouyang, Yunbo. Scalable sparsity structure learning using Bayesian methods.

Degree: PhD, Statistics, 2018, University of Illinois – Urbana-Champaign

 Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on… (more)

Subjects/Keywords: Bayesian statistics; high-dimensional data analysis; variable selection

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APA (6th Edition):

Ouyang, Y. (2018). Scalable sparsity structure learning using Bayesian methods. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101264

Chicago Manual of Style (16th Edition):

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 28, 2021. http://hdl.handle.net/2142/101264.

MLA Handbook (7th Edition):

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Web. 28 Feb 2021.

Vancouver:

Ouyang Y. Scalable sparsity structure learning using Bayesian methods. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/2142/101264.

Council of Science Editors:

Ouyang Y. Scalable sparsity structure learning using Bayesian methods. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101264


Texas A&M University

29. Song, Qifan. Variable Selection for Ultra High Dimensional Data.

Degree: PhD, Statistics, 2014, Texas A&M University

 Variable selection plays an important role for the high dimensional data analysis. In this work, we first propose a Bayesian variable selection approach for ultra-high(more)

Subjects/Keywords: High Dimensional Variable Selection; Big Data; Penalized Likelihood Approach; Posterior Consistency

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APA (6th Edition):

Song, Q. (2014). Variable Selection for Ultra High Dimensional Data. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153224

Chicago Manual of Style (16th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Doctoral Dissertation, Texas A&M University. Accessed February 28, 2021. http://hdl.handle.net/1969.1/153224.

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 28 Feb 2021.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/1969.1/153224.

Council of Science Editors:

Song Q. Variable Selection for Ultra High Dimensional Data. [Doctoral Dissertation]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153224


McMaster University

30. Pichika, Sathish chandra. Sparse Canonical Correlation Analysis (SCCA): A Comparative Study.

Degree: MSc, 2011, McMaster University

Canonical Correlation Analysis (CCA) is one of the multivariate statistical methods that can be used to find relationship between two sets of variables. I… (more)

Subjects/Keywords: CCA; SCCA; High-Dimensional; Multivariate Analysis; Multivariate Analysis

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APA (6th Edition):

Pichika, S. c. (2011). Sparse Canonical Correlation Analysis (SCCA): A Comparative Study. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/11779

Chicago Manual of Style (16th Edition):

Pichika, Sathish chandra. “Sparse Canonical Correlation Analysis (SCCA): A Comparative Study.” 2011. Masters Thesis, McMaster University. Accessed February 28, 2021. http://hdl.handle.net/11375/11779.

MLA Handbook (7th Edition):

Pichika, Sathish chandra. “Sparse Canonical Correlation Analysis (SCCA): A Comparative Study.” 2011. Web. 28 Feb 2021.

Vancouver:

Pichika Sc. Sparse Canonical Correlation Analysis (SCCA): A Comparative Study. [Internet] [Masters thesis]. McMaster University; 2011. [cited 2021 Feb 28]. Available from: http://hdl.handle.net/11375/11779.

Council of Science Editors:

Pichika Sc. Sparse Canonical Correlation Analysis (SCCA): A Comparative Study. [Masters Thesis]. McMaster University; 2011. Available from: http://hdl.handle.net/11375/11779

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