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You searched for subject:(High dimensional statistics). Showing records 1 – 30 of 109 total matches.

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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

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 (6th 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 (16th 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 (7th 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

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

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

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APA (6th Edition):

Gaynanova, I. (2015). Estimation Of Sparse Low-Dimensional Linear Projections . (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 (16th 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 (7th 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

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


Princeton University

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

Degree: PhD, 2016, Princeton University

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Doctoral Dissertation, Princeton University. Accessed April 21, 2019. http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

MLA Handbook (7th 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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Chen, Sheng. “Computational and Statistical Aspects of High-Dimensional Structured Estimation.” 2018. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/198991.

MLA Handbook (7th 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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/172631.

MLA Handbook (7th 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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/165147.

MLA Handbook (7th 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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Doctoral Dissertation, University of Michigan. Accessed April 21, 2019. http://hdl.handle.net/2027.42/140828.

MLA Handbook (7th Edition):

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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Bhattacharyya, Sharmodeep. “A Study of High-dimensional Clustering and Statistical Inference on Networks.” 2013. Thesis, University of California – Berkeley. Accessed April 21, 2019. http://www.escholarship.org/uc/item/9sx0k48k.

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

MLA Handbook (7th Edition):

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

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

Council of Science Editors:

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

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


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

 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 (6th 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 (16th 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 (7th 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

  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 (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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


Duke University

14. Banerjee, Anjishnu. Scalable Nonparametric Bayes Learning .

Degree: 2013, Duke University

  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 (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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

  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 (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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

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 (6th 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 (16th 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 (7th 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

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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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


University of Texas – Austin

18. Yang, Eunho. High-dimensional statistics : model specification and elementary estimators.

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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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

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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th Edition):

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

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

Council of Science Editors:

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

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


University of California – Berkeley

26. Loh, Po-LIng. High-dimensional statistics with systematically corrupted data.

Degree: Statistics, 2014, University of California – Berkeley

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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


Princeton University

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

Degree: PhD, 2018, Princeton University

 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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

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;

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APA (6th 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 (16th 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 (7th 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

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