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You searched for subject:(High dimensional data). Showing records 1 – 30 of 231 total matches.

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University of Georgia

1. Safo, Sandra Esi. Design and analysis issues in high dimension, low sample size problems.

Degree: PhD, Statistics, 2014, University of Georgia

 Advancement in technology and computing power have led to the generation of data with enormous amount of variables when compared to the number of observations.… (more)

Subjects/Keywords: High dimensional data

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APA (6th Edition):

Safo, S. E. (2014). Design and analysis issues in high dimension, low sample size problems. (Doctoral Dissertation). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/safo_sandra_e_201408_phd

Chicago Manual of Style (16th Edition):

Safo, Sandra Esi. “Design and analysis issues in high dimension, low sample size problems.” 2014. Doctoral Dissertation, University of Georgia. Accessed February 24, 2020. http://purl.galileo.usg.edu/uga_etd/safo_sandra_e_201408_phd.

MLA Handbook (7th Edition):

Safo, Sandra Esi. “Design and analysis issues in high dimension, low sample size problems.” 2014. Web. 24 Feb 2020.

Vancouver:

Safo SE. Design and analysis issues in high dimension, low sample size problems. [Internet] [Doctoral dissertation]. University of Georgia; 2014. [cited 2020 Feb 24]. Available from: http://purl.galileo.usg.edu/uga_etd/safo_sandra_e_201408_phd.

Council of Science Editors:

Safo SE. Design and analysis issues in high dimension, low sample size problems. [Doctoral Dissertation]. University of Georgia; 2014. Available from: http://purl.galileo.usg.edu/uga_etd/safo_sandra_e_201408_phd

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

Degree: Department of Economics, 2018, Brown University

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

Subjects/Keywords: high dimensional data

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Freyaldenhoven, Simon. “Essays on Factor Models and Latent Variables in Economics.” 2018. Thesis, Brown University. Accessed February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Freyaldenhoven S. Essays on Factor Models and Latent Variables in Economics. [Internet] [Thesis]. Brown University; 2018. [cited 2020 Feb 24]. 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 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Fedoruk JP. Dimensionality Reduction via the Johnson and Lindenstrauss Lemma: Mathematical and Computational Improvements. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2020 Feb 24]. 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 Minnesota

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

Degree: PhD, Statistics, 2014, University of Minnesota

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Doctoral Dissertation, University of Minnesota. Accessed February 24, 2020. http://hdl.handle.net/11299/172631.

MLA Handbook (7th Edition):

Ye, Changqing. “Network selection, information filtering and scalable computation.” 2014. Web. 24 Feb 2020.

Vancouver:

Ye C. Network selection, information filtering and scalable computation. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2020 Feb 24]. 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 Rochester

5. Pearson, Alexander T. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.

Degree: PhD, 2009, University of Rochester

 Identifying those genes that are differentially expressed in individuals with cancer could lead to new avenues of treatment or prevention. Gene array information can be… (more)

Subjects/Keywords: Subset Selection; Gene Array; High Dimensional Data

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APA (6th Edition):

Pearson, A. T. (2009). Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/8411

Chicago Manual of Style (16th Edition):

Pearson, Alexander T. “Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.” 2009. Doctoral Dissertation, University of Rochester. Accessed February 24, 2020. http://hdl.handle.net/1802/8411.

MLA Handbook (7th Edition):

Pearson, Alexander T. “Subset Selection for High-Dimensional Data, with Applications to Gene Array Data.” 2009. Web. 24 Feb 2020.

Vancouver:

Pearson AT. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. [Internet] [Doctoral dissertation]. University of Rochester; 2009. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/1802/8411.

Council of Science Editors:

Pearson AT. Subset Selection for High-Dimensional Data, with Applications to Gene Array Data. [Doctoral Dissertation]. University of Rochester; 2009. Available from: http://hdl.handle.net/1802/8411


Massey University

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

Degree: 2015, Massey University

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Ullah, Insha. “Contributions to high-dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand .” 2015. Thesis, Massey University. Accessed February 24, 2020. 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. 24 Feb 2020.

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 2020 Feb 24]. Available from: http://hdl.handle.net/10179/6608.

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

Council of Science Editors:

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

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


University of 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Qian C. Some Advances on Modeling High-Dimensional Data with Complex Structures. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2020 Feb 24]. 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


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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Blake PM. Biclustering and Visualization of High Dimensional Data using VIsual Statistical Data Analyzer. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2020 Feb 24]. 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 February 24, 2020. http://hdl.handle.net/11299/199089.

MLA Handbook (7th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Web. 24 Feb 2020.

Vancouver:

Datta A. Statistical Methods for Large Complex Datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2020 Feb 24]. 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 February 24, 2020. http://hdl.handle.net/10150/631476.

MLA Handbook (7th Edition):

Washburn, Ammon. “High-Confidence Learning from Uncertain Data with High Dimensionality .” 2018. Web. 24 Feb 2020.

Vancouver:

Washburn A. High-Confidence Learning from Uncertain Data with High Dimensionality . [Internet] [Doctoral dissertation]. University of Arizona; 2018. [cited 2020 Feb 24]. 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 Minnesota

11. 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

O'Connell M. Integrative Analyses for Multi-source Data with Multiple Shared Dimensions. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2020 Feb 24]. 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 Adelaide

12. 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Conway A. Clustering of proteomics imaging mass spectrometry data. [Internet] [Thesis]. University of Adelaide; 2016. [cited 2020 Feb 24]. 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

13. 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Waddell A. Interactive Visualization and Exploration of High-Dimensional Data. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2020 Feb 24]. 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


Tulane University

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

Degree: 2019, Tulane University

[email protected]

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Qu, Zhe. “High-dimensional statistical data integration.” 2019. Thesis, Tulane University. Accessed February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Qu Z. High-dimensional statistical data integration. [Internet] [Thesis]. Tulane University; 2019. [cited 2020 Feb 24]. 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 California – Riverside

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

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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Zakaria, Jesin. “Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data.” 2013. Thesis, University of California – Riverside. Accessed February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Zakaria J. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. [Internet] [Thesis]. University of California – Riverside; 2013. [cited 2020 Feb 24]. Available from: http://www.escholarship.org/uc/item/660316zp.

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

Council of Science Editors:

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

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


University of Southern California

16. 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/5613

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 February 24, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

MLA Handbook (7th Edition):

Ren, Jie. “Robust feature selection with penalized regression in imbalanced high dimensional data.” 2014. Web. 24 Feb 2020.

Vancouver:

Ren J. Robust feature selection with penalized regression in imbalanced high dimensional data. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2020 Feb 24]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/443080/rec/5613.

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/5613


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 February 24, 2020. http://digital.library.temple.edu/u?/p245801coll10,214785.

MLA Handbook (7th Edition):

Lou, Qiang. “LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA.” 2013. Web. 24 Feb 2020.

Vancouver:

Lou Q. LEARNING FROM INCOMPLETE HIGH-DIMENSIONAL DATA. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2020 Feb 24]. 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Shou H. Statistical Methods for Structured Multilevel Functional Data: Estimation and Reliability. [Internet] [Thesis]. Johns Hopkins University; 2014. [cited 2020 Feb 24]. 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


Texas A&M University

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

Degree: 2014, Texas A&M University

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

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

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APA (6th Edition):

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

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):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Thesis, Texas A&M University. Accessed February 24, 2020. http://hdl.handle.net/1969.1/153224.

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

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 24 Feb 2020.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/1969.1/153224.

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

Council of Science Editors:

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

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


University of Minnesota

20. Peng, Bo. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.

Degree: PhD, Statistics, 2016, University of Minnesota

 In recent years, penalized models have gained considerable importance on deal- ing with variable selection and estimation problems under high dimensional settings. Of all the… (more)

Subjects/Keywords: High dimensional data; Non-convex penalty; Oracle property; Quantile regression; SVM

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APA (6th Edition):

Peng, B. (2016). Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182177

Chicago Manual of Style (16th Edition):

Peng, Bo. “Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.” 2016. Doctoral Dissertation, University of Minnesota. Accessed February 24, 2020. http://hdl.handle.net/11299/182177.

MLA Handbook (7th Edition):

Peng, Bo. “Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data.” 2016. Web. 24 Feb 2020.

Vancouver:

Peng B. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/11299/182177.

Council of Science Editors:

Peng B. Methodologies and Algorithms on Some Non-convex Penalized Models for Ultra High Dimensional Data. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182177


Harvard University

21. 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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Minnier J. Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods. [Internet] [Doctoral dissertation]. Harvard University; 2012. [cited 2020 Feb 24]. 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

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

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 February 24, 2020. 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. 24 Feb 2020.

Vancouver:

Sinnott JA. Kernel Machine Methods for Risk Prediction with High Dimensional Data. [Internet] [Doctoral dissertation]. Harvard University; 2012. [cited 2020 Feb 24]. 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


Université Catholique de Louvain

23. Ballarini, Robin. Random intersection trees for genomic data analysis.

Degree: 2016, Université Catholique de Louvain

In Machine Learning classification, searching for informative interactions in large high-dimensional datasets is computationally intensive. Most algorithms that attempt this usually start with an empty… (more)

Subjects/Keywords: machine learning; classification; interactions; random intersection trees; high-dimensional data

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APA (6th Edition):

Ballarini, R. (2016). Random intersection trees for genomic data analysis. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:4593

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):

Ballarini, Robin. “Random intersection trees for genomic data analysis.” 2016. Thesis, Université Catholique de Louvain. Accessed February 24, 2020. http://hdl.handle.net/2078.1/thesis:4593.

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

MLA Handbook (7th Edition):

Ballarini, Robin. “Random intersection trees for genomic data analysis.” 2016. Web. 24 Feb 2020.

Vancouver:

Ballarini R. Random intersection trees for genomic data analysis. [Internet] [Thesis]. Université Catholique de Louvain; 2016. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/2078.1/thesis:4593.

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

Council of Science Editors:

Ballarini R. Random intersection trees for genomic data analysis. [Thesis]. Université Catholique de Louvain; 2016. Available from: http://hdl.handle.net/2078.1/thesis:4593

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


Virginia Tech

24. Sun, Jinhui. Robust Feature Screening Procedures for Mixed Type of Data.

Degree: PhD, Statistics, 2016, Virginia Tech

High dimensional data have been frequently collected in many fields of scientific research and technological development. The traditional idea of best subset selection methods, which… (more)

Subjects/Keywords: ultra-high dimensional variable selection; feature screening; mixed type of data

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APA (6th Edition):

Sun, J. (2016). Robust Feature Screening Procedures for Mixed Type of Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73709

Chicago Manual of Style (16th Edition):

Sun, Jinhui. “Robust Feature Screening Procedures for Mixed Type of Data.” 2016. Doctoral Dissertation, Virginia Tech. Accessed February 24, 2020. http://hdl.handle.net/10919/73709.

MLA Handbook (7th Edition):

Sun, Jinhui. “Robust Feature Screening Procedures for Mixed Type of Data.” 2016. Web. 24 Feb 2020.

Vancouver:

Sun J. Robust Feature Screening Procedures for Mixed Type of Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/10919/73709.

Council of Science Editors:

Sun J. Robust Feature Screening Procedures for Mixed Type of Data. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73709

25. 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 February 24, 2020. http://hdl.handle.net/2097/14039.

MLA Handbook (7th Edition):

Wang, Xiaofei. “Randomization test and correlation effects in high dimensional data.” 2012. Web. 24 Feb 2020.

Vancouver:

Wang X. Randomization test and correlation effects in high dimensional data. [Internet] [Masters thesis]. Kansas State University; 2012. [cited 2020 Feb 24]. 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


University of Colorado

26. Kaslovsky, Daniel N. Geometric Sparsity in High Dimension.

Degree: PhD, Mathematics, 2012, University of Colorado

  While typically complex and high-dimensional, modern data sets often have a concise underlying structure. This thesis explores the sparsity inherent in the geometric structure… (more)

Subjects/Keywords: Geometry; High-dimensional data; Noise; Sparsity; Applied Mathematics

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APA (6th Edition):

Kaslovsky, D. N. (2012). Geometric Sparsity in High Dimension. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/math_gradetds/15

Chicago Manual of Style (16th Edition):

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Doctoral Dissertation, University of Colorado. Accessed February 24, 2020. https://scholar.colorado.edu/math_gradetds/15.

MLA Handbook (7th Edition):

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Web. 24 Feb 2020.

Vancouver:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Internet] [Doctoral dissertation]. University of Colorado; 2012. [cited 2020 Feb 24]. Available from: https://scholar.colorado.edu/math_gradetds/15.

Council of Science Editors:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Doctoral Dissertation]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/math_gradetds/15


Louisiana State University

27. Kaur, Gurminder. Effective Visualization Approaches For Ultra-High Dimensional Datasets.

Degree: PhD, Databases and Information Systems, 2018, Louisiana State University

  Multivariate informational data, which are abstract as well as complex, are becoming increasingly common in many areas such as scientific, medical, social, business, and… (more)

Subjects/Keywords: Computer Visualization; Exploratory Analysis; Multivariate Data Visualization; Ultra-High Dimensional Datasets

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

APA (6th Edition):

Kaur, G. (2018). Effective Visualization Approaches For Ultra-High Dimensional Datasets. (Doctoral Dissertation). Louisiana State University. Retrieved from https://digitalcommons.lsu.edu/gradschool_dissertations/4750

Chicago Manual of Style (16th Edition):

Kaur, Gurminder. “Effective Visualization Approaches For Ultra-High Dimensional Datasets.” 2018. Doctoral Dissertation, Louisiana State University. Accessed February 24, 2020. https://digitalcommons.lsu.edu/gradschool_dissertations/4750.

MLA Handbook (7th Edition):

Kaur, Gurminder. “Effective Visualization Approaches For Ultra-High Dimensional Datasets.” 2018. Web. 24 Feb 2020.

Vancouver:

Kaur G. Effective Visualization Approaches For Ultra-High Dimensional Datasets. [Internet] [Doctoral dissertation]. Louisiana State University; 2018. [cited 2020 Feb 24]. Available from: https://digitalcommons.lsu.edu/gradschool_dissertations/4750.

Council of Science Editors:

Kaur G. Effective Visualization Approaches For Ultra-High Dimensional Datasets. [Doctoral Dissertation]. Louisiana State University; 2018. Available from: https://digitalcommons.lsu.edu/gradschool_dissertations/4750

28. Suyundikov, Anvar. Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study.

Degree: PhD, Mathematics and Statistics, 2015, Utah State University

  The main purpose of this dissertation was to examine the statistical dependence of imputed microRNA (miRNA) data in a colorectal cancer study. The dissertation… (more)

Subjects/Keywords: Statistical Dependence; High-Dimensional Data; Colorectal Cancer Study; Mathematics

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APA (6th Edition):

Suyundikov, A. (2015). Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/4371

Chicago Manual of Style (16th Edition):

Suyundikov, Anvar. “Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study.” 2015. Doctoral Dissertation, Utah State University. Accessed February 24, 2020. https://digitalcommons.usu.edu/etd/4371.

MLA Handbook (7th Edition):

Suyundikov, Anvar. “Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study.” 2015. Web. 24 Feb 2020.

Vancouver:

Suyundikov A. Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study. [Internet] [Doctoral dissertation]. Utah State University; 2015. [cited 2020 Feb 24]. Available from: https://digitalcommons.usu.edu/etd/4371.

Council of Science Editors:

Suyundikov A. Statistical Dependence in Imputed High-Dimensional Data for a Colorectal Cancer Study. [Doctoral Dissertation]. Utah State University; 2015. Available from: https://digitalcommons.usu.edu/etd/4371


University of Waterloo

29. Wang, Xinghao. Conditional Scenario Generation with a GVAR Model.

Degree: 2016, University of Waterloo

 The stress-testing method formed an integral part of the practice of risk management. However, the underlying models for scenarios generation have not been much studied… (more)

Subjects/Keywords: Stress-testing; Conditional Scenario Generation; High-dimensional Data; GVAR

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APA (6th Edition):

Wang, X. (2016). Conditional Scenario Generation with a GVAR Model. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/11108

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, Xinghao. “Conditional Scenario Generation with a GVAR Model.” 2016. Thesis, University of Waterloo. Accessed February 24, 2020. http://hdl.handle.net/10012/11108.

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

MLA Handbook (7th Edition):

Wang, Xinghao. “Conditional Scenario Generation with a GVAR Model.” 2016. Web. 24 Feb 2020.

Vancouver:

Wang X. Conditional Scenario Generation with a GVAR Model. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2020 Feb 24]. Available from: http://hdl.handle.net/10012/11108.

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

Council of Science Editors:

Wang X. Conditional Scenario Generation with a GVAR Model. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/11108

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


Wayne State University

30. Li, Yan. Novel Regression Models For High-Dimensional Survival Analysis.

Degree: PhD, Computer Science, 2016, Wayne State University

  Survival analysis aims to predict the occurrence of specific events of interest at future time points. The presence of incomplete observations due to censoring… (more)

Subjects/Keywords: High-dimensional data; Regularization; sparsity; Survival Analysis; Computer Sciences

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APA (6th Edition):

Li, Y. (2016). Novel Regression Models For High-Dimensional Survival Analysis. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1555

Chicago Manual of Style (16th Edition):

Li, Yan. “Novel Regression Models For High-Dimensional Survival Analysis.” 2016. Doctoral Dissertation, Wayne State University. Accessed February 24, 2020. https://digitalcommons.wayne.edu/oa_dissertations/1555.

MLA Handbook (7th Edition):

Li, Yan. “Novel Regression Models For High-Dimensional Survival Analysis.” 2016. Web. 24 Feb 2020.

Vancouver:

Li Y. Novel Regression Models For High-Dimensional Survival Analysis. [Internet] [Doctoral dissertation]. Wayne State University; 2016. [cited 2020 Feb 24]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1555.

Council of Science Editors:

Li Y. Novel Regression Models For High-Dimensional Survival Analysis. [Doctoral Dissertation]. Wayne State University; 2016. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1555

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