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572 total matches.

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- 2007 – 2011 (87)
- 2002 – 2006 (20)

Universities

- University of Michigan (45)
- National University of Singapore (23)
- University of Minnesota (17)
- Princeton University (15)
- Georgia Tech (13)
- University of Texas – Austin (13)
- Penn State University (12)
- University of Cambridge (12)
- Kansas State University (11)
- Duke University (10)
- University of California – Berkeley (10)

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- Statistics (64)
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Degrees

- PhD (258)
- Docteur es (42)
- MS (26)

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

Degree: Department of Economics, 2018, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:792643/

► 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

Record Details Similar Records

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

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

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

URL: http://hdl.handle.net/2142/108476

► 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

Record Details Similar Records

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

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

URL: http://handle.unsw.edu.au/1959.4/60171 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51009/SOURCE2?view=true

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://era.library.ualberta.ca/files/cm039k5065

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

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

URL: http://hdl.handle.net/2027.42/140828

► 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

Record Details Similar Records

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

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

URL: http://hdl.handle.net/1813/40643

► 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

Record Details Similar Records

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

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

URL: https://submit-etda.libraries.psu.edu/catalog/23542

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/298064

► 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

Record Details Similar Records

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

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

URL: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f

►

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

Record Details Similar Records

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

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

URL: http://hdl.handle.net/11299/165147

► 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

Record Details Similar Records

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

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

URL: http://hdl.handle.net/11299/172631

► 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

Record Details Similar Records

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

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

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

Record Details Similar Records

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

MLA Handbook (7^{th} 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

URL: http://hdl.handle.net/10179/6608

► *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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

► 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

Record Details Similar Records

❌

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

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

URL: http://hdl.handle.net/1803/15939

► 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

Record Details Similar Records

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

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

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

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

URL: http://hdl.handle.net/10919/87392

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

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

URL: http://arks.princeton.edu/ark:/88435/dsp014m90dx99b

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/217800

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10012/16310

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628115-091340

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.escholarship.org/uc/item/660316zp

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.escholarship.org/uc/item/9sx0k48k

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

Tulane University

24.
Qu, Zhe.
* High*-

Degree: 2019, Tulane University

URL: https://digitallibrary.tulane.edu/islandora/object/tulane:106916

►

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://digitallibrary.tulane.edu/islandora/object/tulane:78817

►

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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/2027.42/96097

► 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…

Record Details Similar Records

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

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

URL: http://hdl.handle.net/1853/62196

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2142/101264

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

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

URL: http://hdl.handle.net/1969.1/153224

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11375/11779

►

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

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