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You searched for subject:(High dimensional data). Showing records 1 – 30 of 264 total matches.

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

Degree: Department of Economics, 2018, Brown University

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

Subjects/Keywords: high dimensional data

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Freyaldenhoven, Simon. “Essays on Factor Models and Latent Variables in Economics.” 2018. Thesis, Brown University. Accessed March 02, 2021. https://repository.library.brown.edu/studio/item/bdr:792643/.

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

MLA Handbook (7th Edition):

Freyaldenhoven, Simon. “Essays on Factor Models and Latent Variables in Economics.” 2018. Web. 02 Mar 2021.

Vancouver:

Freyaldenhoven S. Essays on Factor Models and Latent Variables in Economics. [Internet] [Thesis]. Brown University; 2018. [cited 2021 Mar 02]. Available from: https://repository.library.brown.edu/studio/item/bdr:792643/.

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

Council of Science Editors:

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

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


University of Illinois – Urbana-Champaign

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Wang, Runmin. “Statistical inference for high-dimensional data via U-statistcs.” 2020. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 02, 2021. http://hdl.handle.net/2142/108476.

MLA Handbook (7th Edition):

Wang, Runmin. “Statistical inference for high-dimensional data via U-statistcs.” 2020. Web. 02 Mar 2021.

Vancouver:

Wang R. Statistical inference for high-dimensional data via U-statistcs. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/2142/108476.

Council of Science Editors:

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


University of Alberta

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Masters Thesis, University of Alberta. Accessed March 02, 2021. https://era.library.ualberta.ca/files/cm039k5065.

MLA Handbook (7th Edition):

Fedoruk, John P. “Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements.” 2016. Web. 02 Mar 2021.

Vancouver:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2021 Mar 02]. Available from: https://era.library.ualberta.ca/files/cm039k5065.

Council of Science Editors:

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


University of Michigan

4. 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 March 02, 2021. http://hdl.handle.net/2027.42/140828.

MLA Handbook (7th Edition):

Qian, Cheng. “Some Advances on Modeling High-Dimensional Data with Complex Structures.” 2017. Web. 02 Mar 2021.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2021 Mar 02]. 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


Delft University of Technology

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

Degree: 2018, Delft University of Technology

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Masters Thesis, Delft University of Technology. Accessed March 02, 2021. http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.

MLA Handbook (7th Edition):

Grisel, Bastiaan (author). “The analysis of three-dimensional embeddings in Virtual Reality.” 2018. Web. 02 Mar 2021.

Vancouver:

Grisel B(. The analysis of three-dimensional embeddings in Virtual Reality. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 02]. Available from: http://resolver.tudelft.nl/uuid:afad36f5-64c7-4969-9615-93d89b43e65f.

Council of Science Editors:

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


University of Minnesota

6. 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 March 02, 2021. http://hdl.handle.net/11299/172631.

MLA Handbook (7th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Web. 02 Mar 2021.

Vancouver:

Ye C. Network selection, information filtering and scalable computation. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Mar 02]. 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


Massey University

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

Degree: 2015, Massey University

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Ullah, Insha. “Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .” 2015. Thesis, Massey University. Accessed March 02, 2021. http://hdl.handle.net/10179/6608.

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

MLA Handbook (7th Edition):

Ullah, Insha. “Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .” 2015. Web. 02 Mar 2021.

Vancouver:

Ullah I. Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand . [Internet] [Thesis]. Massey University; 2015. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/10179/6608.

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

Council of Science Editors:

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

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


Virginia Tech

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

Degree: MS, Electrical Engineering, 2019, Virginia Tech

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Masters Thesis, Virginia Tech. Accessed March 02, 2021. http://hdl.handle.net/10919/87392.

MLA Handbook (7th Edition):

Blake, Patrick Michael. “Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer.” 2019. Web. 02 Mar 2021.

Vancouver:

Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/10919/87392.

Council of Science Editors:

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


University of Minnesota

9. Datta, Abhirup. Statistical Methods for Large Complex Datasets.

Degree: PhD, Biostatistics, 2016, University of Minnesota

 Modern technological advancements have enabled massive-scale collection, processing and storage of information triggering the onset of the `big data' era where in every two days… (more)

Subjects/Keywords: Big data; High dimensional data; Large spatial data

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APA (6th Edition):

Datta, A. (2016). Statistical Methods for Large Complex Datasets. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/199089

Chicago Manual of Style (16th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Doctoral Dissertation, University of Minnesota. Accessed March 02, 2021. http://hdl.handle.net/11299/199089.

MLA Handbook (7th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Web. 02 Mar 2021.

Vancouver:

Datta A. Statistical Methods for Large Complex Datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/11299/199089.

Council of Science Editors:

Datta A. Statistical Methods for Large Complex Datasets. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/199089


University of Arizona

10. Washburn, Ammon. High-Confidence Learning from Uncertain Data with High Dimensionality .

Degree: 2018, University of Arizona

 Some of the most challenging issues in big data are size, scalability and reliability. Big data, such as pictures, videos, and text, have innate structure… (more)

Subjects/Keywords: data classification; data uncertainty; high dimensional data; machine learning; optimization

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APA (6th Edition):

Washburn, A. (2018). High-Confidence Learning from Uncertain Data with High Dimensionality . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/631476

Chicago Manual of Style (16th Edition):

Washburn, Ammon. “High-Confidence Learning from Uncertain Data with High Dimensionality .” 2018. Doctoral Dissertation, University of Arizona. Accessed March 02, 2021. http://hdl.handle.net/10150/631476.

MLA Handbook (7th Edition):

Washburn, Ammon. “High-Confidence Learning from Uncertain Data with High Dimensionality .” 2018. Web. 02 Mar 2021.

Vancouver:

Washburn A. High-Confidence Learning from Uncertain Data with High Dimensionality . [Internet] [Doctoral dissertation]. University of Arizona; 2018. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/10150/631476.

Council of Science Editors:

Washburn A. High-Confidence Learning from Uncertain Data with High Dimensionality . [Doctoral Dissertation]. University of Arizona; 2018. Available from: http://hdl.handle.net/10150/631476


University of California – Riverside

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Zakaria, Jesin. “Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.” 2013. Thesis, University of California – Riverside. Accessed March 02, 2021. http://www.escholarship.org/uc/item/660316zp.

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

MLA Handbook (7th Edition):

Zakaria, Jesin. “Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.” 2013. Web. 02 Mar 2021.

Vancouver:

Zakaria J. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. [Internet] [Thesis]. University of California – Riverside; 2013. [cited 2021 Mar 02]. Available from: http://www.escholarship.org/uc/item/660316zp.

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

Council of Science Editors:

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

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


Tulane University

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

Degree: 2019, Tulane University

[email protected]

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Qu, Zhe. “High-dimensional statistical data integration.” 2019. Thesis, Tulane University. Accessed March 02, 2021. https://digitallibrary.tulane.edu/islandora/object/tulane:106916.

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

MLA Handbook (7th Edition):

Qu, Zhe. “High-dimensional statistical data integration.” 2019. Web. 02 Mar 2021.

Vancouver:

Qu Z. High-dimensional statistical data integration. [Internet] [Thesis]. Tulane University; 2019. [cited 2021 Mar 02]. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:106916.

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

Council of Science Editors:

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

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


University of Adelaide

13. Conway, Annie. Clustering of proteomics imaging mass spectrometry data.

Degree: 2016, University of Adelaide

 This thesis presents a toolbox for the exploratory analysis of multivariate data, in particular proteomics imaging mass spectrometry data. Typically such data consist of 15000… (more)

Subjects/Keywords: clustering; proteomics; multivariate data analysis; high-dimensional data analysis; machine learning

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APA (6th Edition):

Conway, A. (2016). Clustering of proteomics imaging mass spectrometry data. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/112036

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):

Conway, Annie. “Clustering of proteomics imaging mass spectrometry data.” 2016. Thesis, University of Adelaide. Accessed March 02, 2021. http://hdl.handle.net/2440/112036.

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

MLA Handbook (7th Edition):

Conway, Annie. “Clustering of proteomics imaging mass spectrometry data.” 2016. Web. 02 Mar 2021.

Vancouver:

Conway A. Clustering of proteomics imaging mass spectrometry data. [Internet] [Thesis]. University of Adelaide; 2016. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/2440/112036.

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

Council of Science Editors:

Conway A. Clustering of proteomics imaging mass spectrometry data. [Thesis]. University of Adelaide; 2016. Available from: http://hdl.handle.net/2440/112036

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


University of Minnesota

14. O'Connell, Michael. Integrative Analyses for Multi-source Data with Multiple Shared Dimensions.

Degree: PhD, Biostatistics, 2018, University of Minnesota

High dimensional data consists of matrices with a large number of features and is common across many fields of study, including genetics, imaging, and toxicology.… (more)

Subjects/Keywords: data integration; high-dimensional data; matrix decomposition; multi-source

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APA (6th Edition):

O'Connell, M. (2018). Integrative Analyses for Multi-source Data with Multiple Shared Dimensions. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/200286

Chicago Manual of Style (16th Edition):

O'Connell, Michael. “Integrative Analyses for Multi-source Data with Multiple Shared Dimensions.” 2018. Doctoral Dissertation, University of Minnesota. Accessed March 02, 2021. http://hdl.handle.net/11299/200286.

MLA Handbook (7th Edition):

O'Connell, Michael. “Integrative Analyses for Multi-source Data with Multiple Shared Dimensions.” 2018. Web. 02 Mar 2021.

Vancouver:

O'Connell M. Integrative Analyses for Multi-source Data with Multiple Shared Dimensions. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/11299/200286.

Council of Science Editors:

O'Connell M. Integrative Analyses for Multi-source Data with Multiple Shared Dimensions. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/200286


University of Southern California

15. Ren, Jie. Robust feature selection with penalized regression in imbalanced high dimensional data.

Degree: PhD, Statistical Genetics and Genetic Epidemiology, 2014, University of Southern California

 This work is motivated by an ongoing USC/Illumina study of prostate cancer recurrence after radical prostatectomy. The study generated gene expression data for nearly thirty… (more)

Subjects/Keywords: feature selection; penalized regression; imbalanced data; high dimensional data; stability selection

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APA (6th Edition):

Ren, J. (2014). Robust feature selection with penalized regression in imbalanced high dimensional data. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5620

Chicago Manual of Style (16th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Doctoral Dissertation, University of Southern California. Accessed March 02, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5620.

MLA Handbook (7th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Web. 02 Mar 2021.

Vancouver:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2021 Mar 02]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5620.

Council of Science Editors:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5620

16. Waddell, Adrian. Interactive Visualization and Exploration of High-Dimensional Data.

Degree: 2016, University of Waterloo

 Visualizing data is an essential part of good statistical practice. Plots are useful for revealing structure in the data, checking model assumptions, detecting outliers and… (more)

Subjects/Keywords: Interactive Data Visualization; High-dimensional Data; Statistical Visualization

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APA (6th Edition):

Waddell, A. (2016). Interactive Visualization and Exploration of High-Dimensional Data. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10188

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):

Waddell, Adrian. “Interactive Visualization and Exploration of High-Dimensional Data.” 2016. Thesis, University of Waterloo. Accessed March 02, 2021. http://hdl.handle.net/10012/10188.

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

MLA Handbook (7th Edition):

Waddell, Adrian. “Interactive Visualization and Exploration of High-Dimensional Data.” 2016. Web. 02 Mar 2021.

Vancouver:

Waddell A. Interactive Visualization and Exploration of High-Dimensional Data. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/10012/10188.

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

Council of Science Editors:

Waddell A. Interactive Visualization and Exploration of High-Dimensional Data. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10188

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


Temple University

17. Lou, Qiang. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.

Degree: PhD, 2013, Temple University

Computer and Information Science

Data sets with irrelevant and redundant features and large fraction of missing values are common in the real life application. Learning… (more)

Subjects/Keywords: Computer science; data mining; feature selection; high dimensional data; incomplete data; machine learning

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

APA (6th Edition):

Lou, Q. (2013). LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,214785

Chicago Manual of Style (16th Edition):

Lou, Qiang. “LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.” 2013. Doctoral Dissertation, Temple University. Accessed March 02, 2021. http://digital.library.temple.edu/u?/p245801coll10,214785.

MLA Handbook (7th Edition):

Lou, Qiang. “LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.” 2013. Web. 02 Mar 2021.

Vancouver:

Lou Q. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2021 Mar 02]. Available from: http://digital.library.temple.edu/u?/p245801coll10,214785.

Council of Science Editors:

Lou Q. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,214785

18. Shou, Haochang. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.

Degree: 2014, Johns Hopkins University

 The thesis investigates a specific type of functional data with multilevel structures induced by complex experimental designs. Novel statistical methods based on principal component analysis… (more)

Subjects/Keywords: functional data analysis; multilevel and structured data; high-dimensional data; imaging reproducibility; shrinkage estimation

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APA (6th Edition):

Shou, H. (2014). Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/37867

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):

Shou, Haochang. “Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.” 2014. Thesis, Johns Hopkins University. Accessed March 02, 2021. http://jhir.library.jhu.edu/handle/1774.2/37867.

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

MLA Handbook (7th Edition):

Shou, Haochang. “Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability.” 2014. Web. 02 Mar 2021.

Vancouver:

Shou H. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. [Internet] [Thesis]. Johns Hopkins University; 2014. [cited 2021 Mar 02]. Available from: http://jhir.library.jhu.edu/handle/1774.2/37867.

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

Council of Science Editors:

Shou H. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. [Thesis]. Johns Hopkins University; 2014. Available from: http://jhir.library.jhu.edu/handle/1774.2/37867

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


NSYSU

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Thesis, NSYSU. Accessed March 02, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

MLA Handbook (7th Edition):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Web. 02 Mar 2021.

Vancouver:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Internet] [Thesis]. NSYSU; 2013. [cited 2021 Mar 02]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

Council of Science Editors:

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

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


Tulane University

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

Degree: 2018, Tulane University

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

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

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Xu, Chao. “Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.” 2018. Thesis, Tulane University. Accessed March 02, 2021. https://digitallibrary.tulane.edu/islandora/object/tulane:78817.

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

MLA Handbook (7th Edition):

Xu, Chao. “Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.” 2018. Web. 02 Mar 2021.

Vancouver:

Xu C. Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits. [Internet] [Thesis]. Tulane University; 2018. [cited 2021 Mar 02]. Available from: https://digitallibrary.tulane.edu/islandora/object/tulane:78817.

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

Council of Science Editors:

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

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

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

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

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

Subjects/Keywords: High Dimensional Data; Engineering

…foundation to analyze and understand the practice. When random data from a high dimensional space… …representations for high-dimensional data are based on linear models. For example, principal component… …and Alfred O. Hero III (2012). “Shortest path for high-dimensional data… …dimensional structure in the data. This thesis explores data representations using differential… …analysis extends the idea and assumes the data lies in some curved non-flat lower dimensional… 

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Doctoral Dissertation, University of Michigan. Accessed March 02, 2021. http://hdl.handle.net/2027.42/96097.

MLA Handbook (7th Edition):

Hwang, Sung Jin. “Geometric Representations of High Dimensional Random Data.” 2012. Web. 02 Mar 2021.

Vancouver:

Hwang SJ. Geometric Representations of High Dimensional Random Data. [Internet] [Doctoral dissertation]. University of Michigan; 2012. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/2027.42/96097.

Council of Science Editors:

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


University of Illinois – Urbana-Champaign

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 02, 2021. http://hdl.handle.net/2142/101264.

MLA Handbook (7th Edition):

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Web. 02 Mar 2021.

Vancouver:

Ouyang Y. Scalable sparsity structure learning using Bayesian methods. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/2142/101264.

Council of Science Editors:

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


Texas A&M University

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

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

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

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Doctoral Dissertation, Texas A&M University. Accessed March 02, 2021. http://hdl.handle.net/1969.1/153224.

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 02 Mar 2021.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/1969.1/153224.

Council of Science Editors:

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


Penn State University

24. Guha Thakurta, Abhradeep. Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression.

Degree: 2012, Penn State University

 Learning systems are the backbone of most web-scale advertisement and recommendation systems. Such systems rely on past inputs from users to decide on a particular… (more)

Subjects/Keywords: Data Privacy; Differential Privacy; Machine Learning; High-dimensional Statistics; Sparse Regression

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APA (6th Edition):

Guha Thakurta, A. (2012). Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16390

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):

Guha Thakurta, Abhradeep. “Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression.” 2012. Thesis, Penn State University. Accessed March 02, 2021. https://submit-etda.libraries.psu.edu/catalog/16390.

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

MLA Handbook (7th Edition):

Guha Thakurta, Abhradeep. “Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression.” 2012. Web. 02 Mar 2021.

Vancouver:

Guha Thakurta A. Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Mar 02]. Available from: https://submit-etda.libraries.psu.edu/catalog/16390.

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

Council of Science Editors:

Guha Thakurta A. Differentially Private Convex Optimization For Empirical Risk Minimization And High-dimensional Regression. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/16390

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


Penn State University

25. Chu, Wanghuan. Feature Screening For Ultra-high Dimensional Longitudinal Data.

Degree: 2016, Penn State University

High and ultrahigh dimensional data analysis is now receiving more and more attention in many scientific fields. Various variable selection methods have been proposed for… (more)

Subjects/Keywords: Feature screening; ultra-high dimensional data; longitudinal genetic study

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APA (6th Edition):

Chu, W. (2016). Feature Screening For Ultra-high Dimensional Longitudinal Data. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/3197xm04j

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):

Chu, Wanghuan. “Feature Screening For Ultra-high Dimensional Longitudinal Data.” 2016. Thesis, Penn State University. Accessed March 02, 2021. https://submit-etda.libraries.psu.edu/catalog/3197xm04j.

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

MLA Handbook (7th Edition):

Chu, Wanghuan. “Feature Screening For Ultra-high Dimensional Longitudinal Data.” 2016. Web. 02 Mar 2021.

Vancouver:

Chu W. Feature Screening For Ultra-high Dimensional Longitudinal Data. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Mar 02]. Available from: https://submit-etda.libraries.psu.edu/catalog/3197xm04j.

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

Council of Science Editors:

Chu W. Feature Screening For Ultra-high Dimensional Longitudinal Data. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/3197xm04j

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


Penn State University

26. Li, Jiahan. THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES .

Degree: 2011, Penn State University

 Recently, genome-wide association studies (GWAS) have successfully identified genes that may affect complex traits or diseases. However, the standard statistical tests for each single-nucleotide polymorphism… (more)

Subjects/Keywords: lasso; variable selection; Bayesian approach; high-dimensional data

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

APA (6th Edition):

Li, J. (2011). THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12143

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):

Li, Jiahan. “THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES .” 2011. Thesis, Penn State University. Accessed March 02, 2021. https://submit-etda.libraries.psu.edu/catalog/12143.

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

MLA Handbook (7th Edition):

Li, Jiahan. “THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES .” 2011. Web. 02 Mar 2021.

Vancouver:

Li J. THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES . [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Mar 02]. Available from: https://submit-etda.libraries.psu.edu/catalog/12143.

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

Council of Science Editors:

Li J. THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES . [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/12143

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


University of California – San Diego

27. Hou, Jue. Modern Statistical Methods for Complex Survival Data.

Degree: Mathematics, 2019, University of California – San Diego

 With the booming of big complex data, various Statistical methods and Data Science techniques have been developed to retrieve valuable information from them.The progress is… (more)

Subjects/Keywords: Mathematics; Statistics; Average treatment effect; High-dimensional data; Inference; Left-truncation

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APA (6th Edition):

Hou, J. (2019). Modern Statistical Methods for Complex Survival Data. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/2qj8m7vs

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):

Hou, Jue. “Modern Statistical Methods for Complex Survival Data.” 2019. Thesis, University of California – San Diego. Accessed March 02, 2021. http://www.escholarship.org/uc/item/2qj8m7vs.

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

MLA Handbook (7th Edition):

Hou, Jue. “Modern Statistical Methods for Complex Survival Data.” 2019. Web. 02 Mar 2021.

Vancouver:

Hou J. Modern Statistical Methods for Complex Survival Data. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2021 Mar 02]. Available from: http://www.escholarship.org/uc/item/2qj8m7vs.

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

Council of Science Editors:

Hou J. Modern Statistical Methods for Complex Survival Data. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/2qj8m7vs

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


Victoria University of Wellington

28. Tran, Binh Ngan. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.

Degree: 2018, Victoria University of Wellington

 More and more high-dimensional data appears in machine learning, especially in classification tasks. With thousands of features, these datasets bring challenges to learning algorithms not… (more)

Subjects/Keywords: Evolutionary Computation; Feature selection; Feature construction; Classification; High-dimensional data

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

APA (6th Edition):

Tran, B. N. (2018). Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7078

Chicago Manual of Style (16th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed March 02, 2021. http://hdl.handle.net/10063/7078.

MLA Handbook (7th Edition):

Tran, Binh Ngan. “Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data.” 2018. Web. 02 Mar 2021.

Vancouver:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2021 Mar 02]. Available from: http://hdl.handle.net/10063/7078.

Council of Science Editors:

Tran BN. Evolutionary Computation for Feature Manipulation in Classification on High-dimensional Data. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7078


Harvard University

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

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 March 02, 2021. 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. 02 Mar 2021.

Vancouver:

Minnier J. Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods. [Internet] [Doctoral dissertation]. Harvard University; 2012. [cited 2021 Mar 02]. 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


Harvard University

30. Sinnott, Jennifer Anne. Kernel Machine Methods for Risk Prediction with High Dimensional Data.

Degree: PhD, Biostatistics, 2012, Harvard University

 Understanding the relationship between genomic markers and complex disease could have a profound impact on medicine, but the large number of potential markers can make… (more)

Subjects/Keywords: high dimensional data; kernel machines; pathways; risk prediction; biostatistics

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APA (6th Edition):

Sinnott, J. A. (2012). Kernel Machine Methods for Risk Prediction with High Dimensional Data. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:9793867

Chicago Manual of Style (16th Edition):

Sinnott, Jennifer Anne. “Kernel Machine Methods for Risk Prediction with High Dimensional Data.” 2012. Doctoral Dissertation, Harvard University. Accessed March 02, 2021. http://nrs.harvard.edu/urn-3:HUL.InstRepos:9793867.

MLA Handbook (7th Edition):

Sinnott, Jennifer Anne. “Kernel Machine Methods for Risk Prediction with High Dimensional Data.” 2012. Web. 02 Mar 2021.

Vancouver:

Sinnott JA. Kernel Machine Methods for Risk Prediction with High Dimensional Data. [Internet] [Doctoral dissertation]. Harvard University; 2012. [cited 2021 Mar 02]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9793867.

Council of Science Editors:

Sinnott JA. Kernel Machine Methods for Risk Prediction with High Dimensional Data. [Doctoral Dissertation]. Harvard University; 2012. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:9793867

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