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University of Melbourne

1.
Miller, Hugh Richard.
Statistical methods for the analysis of *high*-*dimensional* data.

Degree: 2010, University of Melbourne

URL: http://hdl.handle.net/11343/35462

► *High*-*dimensional* *statistics* has captured the imagination of many statisticians worldwide, because of its interesting applications as well as the unique challenges faced. This thesis addresses…
(more)

Subjects/Keywords: high dimensional statistics; nonparametric statistics; ranking; bootstrap

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APA (6^{th} Edition):

Miller, H. R. (2010). Statistical methods for the analysis of high-dimensional data. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/35462

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

Miller, Hugh Richard. “Statistical methods for the analysis of high-dimensional data.” 2010. Doctoral Dissertation, University of Melbourne. Accessed April 21, 2019. http://hdl.handle.net/11343/35462.

MLA Handbook (7^{th} Edition):

Miller, Hugh Richard. “Statistical methods for the analysis of high-dimensional data.” 2010. Web. 21 Apr 2019.

Vancouver:

Miller HR. Statistical methods for the analysis of high-dimensional data. [Internet] [Doctoral dissertation]. University of Melbourne; 2010. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11343/35462.

Council of Science Editors:

Miller HR. Statistical methods for the analysis of high-dimensional data. [Doctoral Dissertation]. University of Melbourne; 2010. Available from: http://hdl.handle.net/11343/35462

Cornell University

2.
Gaynanova, Irina.
Estimation Of Sparse Low-*Dimensional* Linear Projections
.

Degree: 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

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APA (6^{th} Edition):

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

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):

Gaynanova, Irina. “Estimation Of Sparse Low-Dimensional Linear Projections .” 2015. Thesis, Cornell University. Accessed April 21, 2019. http://hdl.handle.net/1813/40643.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Gaynanova, Irina. “Estimation Of Sparse Low-Dimensional Linear Projections .” 2015. Web. 21 Apr 2019.

Vancouver:

Gaynanova I. Estimation Of Sparse Low-Dimensional Linear Projections . [Internet] [Thesis]. Cornell University; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1813/40643.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

3.
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 April 21, 2019. http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

MLA Handbook (7^{th} Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Web. 21 Apr 2019.

Vancouver:

Li Y. Optimal Learning in High Dimensions . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Apr 21]. 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

4.
Chen, Sheng.
Computational and Statistical Aspects of *High*-*Dimensional* Structured Estimation.

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

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

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

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

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APA (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 April 21, 2019. http://hdl.handle.net/11299/198991.

MLA Handbook (7^{th} Edition):

Chen, Sheng. “Computational and Statistical Aspects of High-Dimensional Structured Estimation.” 2018. Web. 21 Apr 2019.

Vancouver:

Chen S. Computational and Statistical Aspects of High-Dimensional Structured Estimation. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11299/198991.

Council of Science Editors:

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

University of Minnesota

5. 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

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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 April 21, 2019. http://hdl.handle.net/11299/172631.

MLA Handbook (7^{th} Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Web. 21 Apr 2019.

Vancouver:

Ye C. Network selection, information filtering and scalable computation. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Apr 21]. 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

6.
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

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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 April 21, 2019. http://hdl.handle.net/11299/165147.

MLA Handbook (7^{th} Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Web. 21 Apr 2019.

Vancouver:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Apr 21]. 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 Michigan

7.
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

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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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Apr 21]. 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

8.
Lee, Wayne Tai.
Bayesian Analysis in Problems with *High* *Dimensional* Data and Complex Dependence Structure.

Degree: Statistics, 2013, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/1mp792b6

► This dissertation is a compilation of three different applied statistical problems from the Bayesian perspective. Although the statistical question in each problem is different, a…
(more)

Subjects/Keywords: Statistics; Bayesian Statistics; Global Extreme; Hierarchical Multilabel Classification; High Dimensional; Spatial Statistics; Surface Wind

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APA (6^{th} Edition):

Lee, W. T. (2013). Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/1mp792b6

Not specified: Masters Thesis or Doctoral Dissertation

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

Lee, Wayne Tai. “Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure.” 2013. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/1mp792b6.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lee, Wayne Tai. “Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure.” 2013. Web. 21 Apr 2019.

Vancouver:

Lee WT. Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/1mp792b6.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lee WT. Bayesian Analysis in Problems with High Dimensional Data and Complex Dependence Structure. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/1mp792b6

Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

9.
Lin, Lina.
Methods for estimation and inference for *high*-*dimensional* models.

Degree: PhD, 2018, University of Washington

URL: http://hdl.handle.net/1773/40975

► This thesis tackles three different problems in *high*-*dimensional* *statistics*. The first two parts of the thesis focus on estimation of sparse *high*-*dimensional* undirected graphical models…
(more)

Subjects/Keywords: Graphical models; High-dimensional statistics; Linear mixed effect models; Regularization; Statistics; Statistics

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

APA (6^{th} Edition):

Lin, L. (2018). Methods for estimation and inference for high-dimensional models. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40975

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

Lin, Lina. “Methods for estimation and inference for high-dimensional models.” 2018. Doctoral Dissertation, University of Washington. Accessed April 21, 2019. http://hdl.handle.net/1773/40975.

MLA Handbook (7^{th} Edition):

Lin, Lina. “Methods for estimation and inference for high-dimensional models.” 2018. Web. 21 Apr 2019.

Vancouver:

Lin L. Methods for estimation and inference for high-dimensional models. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1773/40975.

Council of Science Editors:

Lin L. Methods for estimation and inference for high-dimensional models. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/40975

Bowling Green State University

10. Liu, Yang. Improving the Accuracy of Variable Selection Using the Whole Solution Path.

Degree: PhD, Statistics, 2015, Bowling Green State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

► The performances of penalized least squares approaches profoundly depend on the selection of the tuning parameter; however, statisticians did not reach consensus on the criterion…
(more)

Subjects/Keywords: Statistics; variable selection; high dimensional data; SPSP; AIS

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

APA (6^{th} Edition):

Liu, Y. (2015). Improving the Accuracy of Variable Selection Using the Whole Solution Path. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

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

Liu, Yang. “Improving the Accuracy of Variable Selection Using the Whole Solution Path.” 2015. Doctoral Dissertation, Bowling Green State University. Accessed April 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170.

MLA Handbook (7^{th} Edition):

Liu, Yang. “Improving the Accuracy of Variable Selection Using the Whole Solution Path.” 2015. Web. 21 Apr 2019.

Vancouver:

Liu Y. Improving the Accuracy of Variable Selection Using the Whole Solution Path. [Internet] [Doctoral dissertation]. Bowling Green State University; 2015. [cited 2019 Apr 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170.

Council of Science Editors:

Liu Y. Improving the Accuracy of Variable Selection Using the Whole Solution Path. [Doctoral Dissertation]. Bowling Green State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

University of California – Berkeley

11.
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

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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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Bhattacharyya S. A Study of High-dimensional Clustering and Statistical Inference on Networks. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Apr 21]. 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

Harvard University

12.
Minnier, Jessica.
Inference and Prediction for *High* *Dimensional* Data via Penalized Regression and Kernel Machine Methods.

Degree: PhD, Biostatistics, 2012, Harvard University

URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010

► Analysis of *high* *dimensional* data often seeks to identify a subset of important features and assess their effects on the outcome. Furthermore, the ultimate goal…
(more)

Subjects/Keywords: biostatistics; high dimensional data; kernel machine learning; prediction; statistical genetics; statistics

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APA (6^{th} Edition):

Minnier, J. (2012). Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010

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

Minnier, Jessica. “Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods.” 2012. Doctoral Dissertation, Harvard University. Accessed April 21, 2019. http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010.

MLA Handbook (7^{th} Edition):

Minnier, Jessica. “Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods.” 2012. Web. 21 Apr 2019.

Vancouver:

Minnier J. Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods. [Internet] [Doctoral dissertation]. Harvard University; 2012. [cited 2019 Apr 21]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010.

Council of Science Editors:

Minnier J. Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods. [Doctoral Dissertation]. Harvard University; 2012. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010

Duke University

13.
Shi, Minghui.
Bayesian Sparse Learning for *High* *Dimensional* Data
.

Degree: 2011, Duke University

URL: http://hdl.handle.net/10161/3869

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

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

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Shi, Minghui. “Bayesian Sparse Learning for High Dimensional Data .” 2011. Thesis, Duke University. Accessed April 21, 2019. http://hdl.handle.net/10161/3869.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Shi, Minghui. “Bayesian Sparse Learning for High Dimensional Data .” 2011. Web. 21 Apr 2019.

Vancouver:

Shi M. Bayesian Sparse Learning for High Dimensional Data . [Internet] [Thesis]. Duke University; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10161/3869.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

Duke University

14. Banerjee, Anjishnu. Scalable Nonparametric Bayes Learning .

Degree: 2013, Duke University

URL: http://hdl.handle.net/10161/7177

► Capturing *high* *dimensional* complex ensembles of data is becoming commonplace in a variety of application areas. Some examples include biological studies exploring relationships between…
(more)

Subjects/Keywords: Statistics; Bayes; Gaussian process; high-dimensional; nonparametric; random projections; tensor factorization

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

APA (6^{th} Edition):

Banerjee, A. (2013). Scalable Nonparametric Bayes Learning . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/7177

Not specified: Masters Thesis or Doctoral Dissertation

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

Banerjee, Anjishnu. “Scalable Nonparametric Bayes Learning .” 2013. Thesis, Duke University. Accessed April 21, 2019. http://hdl.handle.net/10161/7177.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Banerjee, Anjishnu. “Scalable Nonparametric Bayes Learning .” 2013. Web. 21 Apr 2019.

Vancouver:

Banerjee A. Scalable Nonparametric Bayes Learning . [Internet] [Thesis]. Duke University; 2013. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10161/7177.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Banerjee A. Scalable Nonparametric Bayes Learning . [Thesis]. Duke University; 2013. Available from: http://hdl.handle.net/10161/7177

Not specified: Masters Thesis or Doctoral Dissertation

Duke University

15.
Wang, Ye.
Bayesian Computation for *High*-*Dimensional* Continuous & Sparse Count Data
.

Degree: 2018, Duke University

URL: http://hdl.handle.net/10161/16853

► Probabilistic modeling of multidimensional data is a common problem in practice. When the data is continuous, one common approach is to suppose that the…
(more)

Subjects/Keywords: Statistics; dimension reduction; high dimensional; manifold learning; MCMC; scalable inference

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

APA (6^{th} Edition):

Wang, Y. (2018). Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16853

Not specified: Masters Thesis or Doctoral Dissertation

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

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Thesis, Duke University. Accessed April 21, 2019. http://hdl.handle.net/10161/16853.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wang, Ye. “Bayesian Computation for High-Dimensional Continuous & Sparse Count Data .” 2018. Web. 21 Apr 2019.

Vancouver:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10161/16853.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Bayesian Computation for High-Dimensional Continuous & Sparse Count Data . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16853

Not specified: Masters Thesis or Doctoral Dissertation

16.
Wang, Xiaofei.
Randomization
test and correlation effects in *high* *dimensional* data.

Degree: MS, Department of Statistics, 2012, Kansas State University

URL: http://hdl.handle.net/2097/14039

► *High*-*dimensional* data (HDD) have been encountered in many fields and are characterized by a “large p, small n” paradigm that arises in genomic, lipidomic, and…
(more)

Subjects/Keywords: Randomization test; Correlation effect; High dimensional data; Statistics (0463)

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APA (6^{th} Edition):

Wang, X. (2012). Randomization test and correlation effects in high dimensional data. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/14039

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

Wang, Xiaofei. “Randomization test and correlation effects in high dimensional data.” 2012. Masters Thesis, Kansas State University. Accessed April 21, 2019. http://hdl.handle.net/2097/14039.

MLA Handbook (7^{th} Edition):

Wang, Xiaofei. “Randomization test and correlation effects in high dimensional data.” 2012. Web. 21 Apr 2019.

Vancouver:

Wang X. Randomization test and correlation effects in high dimensional data. [Internet] [Masters thesis]. Kansas State University; 2012. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2097/14039.

Council of Science Editors:

Wang X. Randomization test and correlation effects in high dimensional data. [Masters Thesis]. Kansas State University; 2012. Available from: http://hdl.handle.net/2097/14039

Uppsala University

17.
Yamazaki, Ryo.
Random Subspace Analysis on Canonical Correlation of *High* *Dimensional* Data.

Degree: Statistics, 2016, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412

► *High* *dimensional*, low sample, data have singular sample covariance matrices,rendering them impossible to analyse by regular canonical correlation (CC). Byusing random subspace method (RSM)…
(more)

Subjects/Keywords: Canonical correlation; Random subspace analysis; high-dimensional statistics

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APA (6^{th} Edition):

Yamazaki, R. (2016). Random Subspace Analysis on Canonical Correlation of High Dimensional Data. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412

Not specified: Masters Thesis or Doctoral Dissertation

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

Yamazaki, Ryo. “Random Subspace Analysis on Canonical Correlation of High Dimensional Data.” 2016. Thesis, Uppsala University. Accessed April 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yamazaki, Ryo. “Random Subspace Analysis on Canonical Correlation of High Dimensional Data.” 2016. Web. 21 Apr 2019.

Vancouver:

Yamazaki R. Random Subspace Analysis on Canonical Correlation of High Dimensional Data. [Internet] [Thesis]. Uppsala University; 2016. [cited 2019 Apr 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yamazaki R. Random Subspace Analysis on Canonical Correlation of High Dimensional Data. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295412

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

18.
Yang, Eunho.
* High*-

Degree: Computer Sciences, 2014, University of Texas – Austin

URL: http://hdl.handle.net/2152/28058

► Modern *statistics* typically deals with complex data, in particular where the ambient dimension of the problem p may be of the same order as, or…
(more)

Subjects/Keywords: High-dimensional statistics; Markov random fields; Graphical models; Closed-form estimators

Record Details Similar Records

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

APA (6^{th} Edition):

Yang, E. (2014). High-dimensional statistics : model specification and elementary estimators. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28058

Not specified: Masters Thesis or Doctoral Dissertation

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

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/28058.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Web. 21 Apr 2019.

Vancouver:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/28058.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28058

Not specified: Masters Thesis or Doctoral Dissertation

University of Pennsylvania

19. Chen, Jun. Statistical Methods for Human Microbiome Data Analysis.

Degree: 2012, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/497

► The human microbiome is the totality of the microbes, their genetic elements and the interactions they have with surrounding environments throughout the human body. Studies…
(more)

Subjects/Keywords: High-dimensional statistics; Metagenomics; Microbiome; Variable selection; Bioinformatics; Biostatistics; Microbiology

Record Details Similar Records

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

APA (6^{th} Edition):

Chen, J. (2012). Statistical Methods for Human Microbiome Data Analysis. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/497

Not specified: Masters Thesis or Doctoral Dissertation

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

Chen, Jun. “Statistical Methods for Human Microbiome Data Analysis.” 2012. Thesis, University of Pennsylvania. Accessed April 21, 2019. https://repository.upenn.edu/edissertations/497.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Chen, Jun. “Statistical Methods for Human Microbiome Data Analysis.” 2012. Web. 21 Apr 2019.

Vancouver:

Chen J. Statistical Methods for Human Microbiome Data Analysis. [Internet] [Thesis]. University of Pennsylvania; 2012. [cited 2019 Apr 21]. Available from: https://repository.upenn.edu/edissertations/497.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen J. Statistical Methods for Human Microbiome Data Analysis. [Thesis]. University of Pennsylvania; 2012. Available from: https://repository.upenn.edu/edissertations/497

Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

20.
Koch, Daniel.
Multiscale methods for the analysis of *high*-*dimensional* locally stationary time series.

Degree: 2015, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/162316

►

This thesis deals with multiscale modelling of the covariance pattern of discrete time series with time-varying autocovariance function. We propose a novel class of locally… (more)

Subjects/Keywords: Locally stationary time series; Wavelet analysis; Multiscale modelling; High-dimensional statistics

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

APA (6^{th} Edition):

Koch, D. (2015). Multiscale methods for the analysis of high-dimensional locally stationary time series. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/162316

Not specified: Masters Thesis or Doctoral Dissertation

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

Koch, Daniel. “Multiscale methods for the analysis of high-dimensional locally stationary time series.” 2015. Thesis, Université Catholique de Louvain. Accessed April 21, 2019. http://hdl.handle.net/2078.1/162316.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Koch, Daniel. “Multiscale methods for the analysis of high-dimensional locally stationary time series.” 2015. Web. 21 Apr 2019.

Vancouver:

Koch D. Multiscale methods for the analysis of high-dimensional locally stationary time series. [Internet] [Thesis]. Université Catholique de Louvain; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2078.1/162316.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Koch D. Multiscale methods for the analysis of high-dimensional locally stationary time series. [Thesis]. Université Catholique de Louvain; 2015. Available from: http://hdl.handle.net/2078.1/162316

Not specified: Masters Thesis or Doctoral Dissertation

University of Alabama

21. Alli, Toyin Omolara. Statistical networks with applications in economics and finance.

Degree: 2016, University of Alabama

URL: http://purl.lib.ua.edu/144969

► Due to the vast amount of economic and financial information to be stored and analyzed, the need for the study of *high* *dimensional* networks has…
(more)

Subjects/Keywords: Electronic Thesis or Dissertation; – thesis; Mathematics; Statistics; economics; high dimensional statistics; regularization

Record Details Similar Records

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APA (6^{th} Edition):

Alli, T. O. (2016). Statistical networks with applications in economics and finance. (Thesis). University of Alabama. Retrieved from http://purl.lib.ua.edu/144969

Not specified: Masters Thesis or Doctoral Dissertation

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

Alli, Toyin Omolara. “Statistical networks with applications in economics and finance.” 2016. Thesis, University of Alabama. Accessed April 21, 2019. http://purl.lib.ua.edu/144969.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Alli, Toyin Omolara. “Statistical networks with applications in economics and finance.” 2016. Web. 21 Apr 2019.

Vancouver:

Alli TO. Statistical networks with applications in economics and finance. [Internet] [Thesis]. University of Alabama; 2016. [cited 2019 Apr 21]. Available from: http://purl.lib.ua.edu/144969.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alli TO. Statistical networks with applications in economics and finance. [Thesis]. University of Alabama; 2016. Available from: http://purl.lib.ua.edu/144969

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

22.
Bloniarz, Adam.
Leveraging latent structure in *high*-*dimensional* data: causality, neuroscience, and nonparametrics.

Degree: Statistics, 2016, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/32f0g2w9

► Many scientific fields have been changed by rapid technological progress in data collection, storage, and processing. This has greatly expanded the role of *statistics* in…
(more)

Subjects/Keywords: Statistics; Neurosciences; Biostatistics; Causal inference; Computational Neuroscience; Distributed computation; 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):

Bloniarz, A. (2016). Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/32f0g2w9

Not specified: Masters Thesis or Doctoral Dissertation

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

Bloniarz, Adam. “Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics.” 2016. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/32f0g2w9.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bloniarz, Adam. “Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics.” 2016. Web. 21 Apr 2019.

Vancouver:

Bloniarz A. Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. [Internet] [Thesis]. University of California – Berkeley; 2016. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/32f0g2w9.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bloniarz A. Leveraging latent structure in high-dimensional data: causality, neuroscience, and nonparametrics. [Thesis]. University of California – Berkeley; 2016. Available from: http://www.escholarship.org/uc/item/32f0g2w9

Not specified: Masters Thesis or Doctoral Dissertation

UCLA

23.
Aragam, Nikhyl Bryon.
Structure Learning of Linear Bayesian Networks in *High*-Dimensions.

Degree: Statistics, 2015, UCLA

URL: http://www.escholarship.org/uc/item/9gs5787w

► Research into graphical models is a rapidly developing enterprise, garnering significant interest from both the *statistics* and machine learning communities. A parallel thread in both…
(more)

Subjects/Keywords: Statistics; Applied mathematics; Bayesian networks; Graphical modeling; High-dimensional statistics; Nonconvex optimization; Regularization; Structure learning

Record Details Similar Records

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

APA (6^{th} Edition):

Aragam, N. B. (2015). Structure Learning of Linear Bayesian Networks in High-Dimensions. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/9gs5787w

Not specified: Masters Thesis or Doctoral Dissertation

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

Aragam, Nikhyl Bryon. “Structure Learning of Linear Bayesian Networks in High-Dimensions.” 2015. Thesis, UCLA. Accessed April 21, 2019. http://www.escholarship.org/uc/item/9gs5787w.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Aragam, Nikhyl Bryon. “Structure Learning of Linear Bayesian Networks in High-Dimensions.” 2015. Web. 21 Apr 2019.

Vancouver:

Aragam NB. Structure Learning of Linear Bayesian Networks in High-Dimensions. [Internet] [Thesis]. UCLA; 2015. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/9gs5787w.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aragam NB. Structure Learning of Linear Bayesian Networks in High-Dimensions. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/9gs5787w

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

24.
Lopes, Miles Edward.
Some Inference Problems in *High*-*Dimensional* Linear Models.

Degree: Statistics, 2015, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/9pg5j6j2

► During the past two decades, technological advances have led to a proliferation of *high*-*dimensional* problems in data analysis. The characteristic feature of such problems is…
(more)

Subjects/Keywords: Statistics; Mathematics; Information science; bootstrap; compressed sensing; high-dimensional statistics; inference; linear model; sparsity

Record Details Similar Records

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

APA (6^{th} Edition):

Lopes, M. E. (2015). Some Inference Problems in High-Dimensional Linear Models. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9pg5j6j2

Not specified: Masters Thesis or Doctoral Dissertation

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

Lopes, Miles Edward. “Some Inference Problems in High-Dimensional Linear Models.” 2015. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/9pg5j6j2.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lopes, Miles Edward. “Some Inference Problems in High-Dimensional Linear Models.” 2015. Web. 21 Apr 2019.

Vancouver:

Lopes ME. Some Inference Problems in High-Dimensional Linear Models. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/9pg5j6j2.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lopes ME. Some Inference Problems in High-Dimensional Linear Models. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/9pg5j6j2

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

25.
Zhu, Ying.
Endogenous Econometric Models and Multi-Stage Estimation in *High*-*Dimensional* Settings: Theory and Applications.

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

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

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

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

Record Details Similar Records

❌

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/9vw1524p.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Web. 21 Apr 2019.

Vancouver:

Zhu Y. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/9vw1524p.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

26.
Loh, Po-LIng.
* High*-

Degree: Statistics, 2014, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/8j49c5n4

► Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling nonidealities in data, such as imputation and expectation-maximization, are…
(more)

Subjects/Keywords: Statistics; Computer science; Electrical engineering; graphical models; high-dimensional statistics; machine learning; optimization

Record Details Similar Records

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

APA (6^{th} Edition):

Loh, P. (2014). High-dimensional statistics with systematically corrupted data. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/8j49c5n4

Not specified: Masters Thesis or Doctoral Dissertation

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

Loh, Po-LIng. “High-dimensional statistics with systematically corrupted data.” 2014. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/8j49c5n4.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Loh, Po-LIng. “High-dimensional statistics with systematically corrupted data.” 2014. Web. 21 Apr 2019.

Vancouver:

Loh P. High-dimensional statistics with systematically corrupted data. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/8j49c5n4.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Loh P. High-dimensional statistics with systematically corrupted data. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/8j49c5n4

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

27. Zhu, Ziwei. Distributed and Robust Statistical Learning .

Degree: PhD, 2018, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

► Decentralized and corrupted data are nowadays ubiquitous, which impose fundamental challenges for modern statistical analysis. Illustrative examples are massive and decentralized data produced by distributed…
(more)

Subjects/Keywords: distributed learning; high-dimensional statistics; low-rank matrix recovery; principal component analysis; regression; robust 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, Z. (2018). Distributed and Robust Statistical Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

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

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Doctoral Dissertation, Princeton University. Accessed April 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

MLA Handbook (7^{th} Edition):

Zhu, Ziwei. “Distributed and Robust Statistical Learning .” 2018. Web. 21 Apr 2019.

Vancouver:

Zhu Z. Distributed and Robust Statistical Learning . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Apr 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x.

Council of Science Editors:

Zhu Z. Distributed and Robust Statistical Learning . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp01d217qs22x

28. Armstrong, Douglas. Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence.

Degree: PhD, Mathematics and Statistics, 2017, South Dakota State University

URL: https://openprairie.sdstate.edu/etd/2175

► The inference of the source of forensic evidence is related to model selection. Many forms of evidence can only be represented by complex, *high*-*dimensional*…
(more)

Subjects/Keywords: Bayes factor; Evidence quantification; Forensic statistics; High-dimensional; Kernel method; Statistical Theory; Statistics and Probability

Record Details Similar Records

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

APA (6^{th} Edition):

Armstrong, D. (2017). Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence. (Doctoral Dissertation). South Dakota State University. Retrieved from https://openprairie.sdstate.edu/etd/2175

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

Armstrong, Douglas. “Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence.” 2017. Doctoral Dissertation, South Dakota State University. Accessed April 21, 2019. https://openprairie.sdstate.edu/etd/2175.

MLA Handbook (7^{th} Edition):

Armstrong, Douglas. “Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence.” 2017. Web. 21 Apr 2019.

Vancouver:

Armstrong D. Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence. [Internet] [Doctoral dissertation]. South Dakota State University; 2017. [cited 2019 Apr 21]. Available from: https://openprairie.sdstate.edu/etd/2175.

Council of Science Editors:

Armstrong D. Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence. [Doctoral Dissertation]. South Dakota State University; 2017. Available from: https://openprairie.sdstate.edu/etd/2175

McMaster University

29.
Yang, Xiao Di.
STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF *HIGH*-*DIMENSIONAL* DATA: LASSO AND EXTENSIONS.

Degree: MSc, 2011, McMaster University

URL: http://hdl.handle.net/11375/11352

►

With the advance of technology, the collection and storage of data has become routine. Huge amount of data are increasingly produced from biological experiments.… (more)

Subjects/Keywords: Lasso; High-Dimensional; Penalized Variable Selection Methods; Applied Statistics; Biostatistics; Statistical Models; Applied Statistics

Record Details Similar Records

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

APA (6^{th} Edition):

Yang, X. D. (2011). STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF HIGH-DIMENSIONAL DATA: LASSO AND EXTENSIONS. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/11352

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

Yang, Xiao Di. “STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF HIGH-DIMENSIONAL DATA: LASSO AND EXTENSIONS.” 2011. Masters Thesis, McMaster University. Accessed April 21, 2019. http://hdl.handle.net/11375/11352.

MLA Handbook (7^{th} Edition):

Yang, Xiao Di. “STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF HIGH-DIMENSIONAL DATA: LASSO AND EXTENSIONS.” 2011. Web. 21 Apr 2019.

Vancouver:

Yang XD. STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF HIGH-DIMENSIONAL DATA: LASSO AND EXTENSIONS. [Internet] [Masters thesis]. McMaster University; 2011. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11375/11352.

Council of Science Editors:

Yang XD. STATISTICAL METHODS FOR VARIABLE SELECTION IN THE CONTEXT OF HIGH-DIMENSIONAL DATA: LASSO AND EXTENSIONS. [Masters Thesis]. McMaster University; 2011. Available from: http://hdl.handle.net/11375/11352

Temple University

30. Spirko, Lauren Nicole. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.

Degree: PhD, 2017, Temple University

URL: http://digital.library.temple.edu/u?/p245801coll10,466860

►

*Statistics*

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes, providing insight into the… (more)

Subjects/Keywords: Statistics; Biostatistics;

Record Details Similar Records

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

APA (6^{th} Edition):

Spirko, L. N. (2017). Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,466860

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

Spirko, Lauren Nicole. “Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.” 2017. Doctoral Dissertation, Temple University. Accessed April 21, 2019. http://digital.library.temple.edu/u?/p245801coll10,466860.

MLA Handbook (7^{th} Edition):

Spirko, Lauren Nicole. “Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes.” 2017. Web. 21 Apr 2019.

Vancouver:

Spirko LN. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2019 Apr 21]. Available from: http://digital.library.temple.edu/u?/p245801coll10,466860.

Council of Science Editors:

Spirko LN. Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. [Doctoral Dissertation]. Temple University; 2017. Available from: http://digital.library.temple.edu/u?/p245801coll10,466860