Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Partial EM Big Data Mixed Effects SCAN ECHO Projection Pursuit PPE Covariacne High dimensional Data Clustering). Showing records 1 – 30 of 149851 total matches.

[1] [2] [3] [4] [5] … [4996]

Search Limiters

Last 2 Years | English Only

Degrees

Languages

Country

▼ Search Limiters

1. Cho, Jang Ik. <em class="hilite"><em class="hilite">Partialem>em> EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia.

Degree: PhD, Epidemiology and Biostatistics, 2018, Case Western Reserve University School of Graduate Studies

 Methodologically, this dissertation contributes to two areas in Statistics: Linear mixed effects models for big data and Test of equal covariance for high-dimensional data. Scientifically,… (more)

Subjects/Keywords: Statistics; Biostatistics; Biomedical Research; Health; Mining; Partial EM, Big Data, Mixed Effects, SCAN, ECHO, Projection Pursuit, PPE, Covariacne, High-dimensional Data, Clustering

Partial EM Procedure for Big-data Linear Mixed Effects Model, and Generalized PPE for High… …Projection Pursuit Ellipse (PPE) to test for equal variance in high-dimensional data is… …to two areas in Statistics: Linear mixed effects models for big data and Test of equal… …capacity. As a solution, we ix proposed a new modern approach to Big-data Linear Mixed Effects… …Bartletts test and a modern benchmark for high dimensional p data. x Part I Big-data Linear… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Cho, J. I. (2018). Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia. (Doctoral Dissertation). Case Western Reserve University School of Graduate Studies. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999

Chicago Manual of Style (16th Edition):

Cho, Jang Ik. “Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia.” 2018. Doctoral Dissertation, Case Western Reserve University School of Graduate Studies. Accessed May 08, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999.

MLA Handbook (7th Edition):

Cho, Jang Ik. “Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia.” 2018. Web. 08 May 2021.

Vancouver:

Cho JI. Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia. [Internet] [Doctoral dissertation]. Case Western Reserve University School of Graduate Studies; 2018. [cited 2021 May 08]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999.

Council of Science Editors:

Cho JI. Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia. [Doctoral Dissertation]. Case Western Reserve University School of Graduate Studies; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999

2. Soledad Espezua Llerena. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.

Degree: 2013, University of São Paulo

Reduzir a dimensão de bancos de dados é um passo importante em processos de reconhecimento de padrões e aprendizagem de máquina. Projection Pursuit (PP) tem… (more)

Subjects/Keywords: Classificação; Dados de microarranjo; Projection Pursuit; Redução dimensional; Classification; Dimentionality reduction; Microarray data; Projection Pursuit

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Llerena, S. E. (2013). Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/

Chicago Manual of Style (16th Edition):

Llerena, Soledad Espezua. “Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.” 2013. Doctoral Dissertation, University of São Paulo. Accessed May 08, 2021. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/.

MLA Handbook (7th Edition):

Llerena, Soledad Espezua. “Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.” 2013. Web. 08 May 2021.

Vancouver:

Llerena SE. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. [Internet] [Doctoral dissertation]. University of São Paulo; 2013. [cited 2021 May 08]. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/.

Council of Science Editors:

Llerena SE. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. [Doctoral Dissertation]. University of São Paulo; 2013. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/


University of Minnesota

3. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 2021. http://hdl.handle.net/11299/199089.

MLA Handbook (7th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Web. 08 May 2021.

Vancouver:

Datta A. Statistical Methods for Large Complex Datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2021 May 08]. 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


KTH

4. Lannsjö, Fredrik. Forecasting the Business Cycle using <em class="hilite">Partialem> Least Squares.

Degree: Mathematical Statistics, 2014, KTH

<em class="hilite">Partialem> Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly… (more)

Subjects/Keywords: Quantitative Forecast; Partial Least Squares; Variable Selection; High-dimensional Regression; Big Data; Business Cycle; Leading Indicators

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lannsjö, F. (2014). Forecasting the Business Cycle using Partial Least Squares. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378

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):

Lannsjö, Fredrik. “Forecasting the Business Cycle using Partial Least Squares.” 2014. Thesis, KTH. Accessed May 08, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378.

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

MLA Handbook (7th Edition):

Lannsjö, Fredrik. “Forecasting the Business Cycle using Partial Least Squares.” 2014. Web. 08 May 2021.

Vancouver:

Lannsjö F. Forecasting the Business Cycle using Partial Least Squares. [Internet] [Thesis]. KTH; 2014. [cited 2021 May 08]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378.

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

Council of Science Editors:

Lannsjö F. Forecasting the Business Cycle using Partial Least Squares. [Thesis]. KTH; 2014. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378

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


Rice University

5. Yang, Yuchen. Convergence of K-indicators Clustering with Alternating Projection Algorithms.

Degree: MA, Engineering, 2017, Rice University

Data clustering is a fundamental unsupervised machine learning problem, and the most widely used method of data clustering over the decades is k-means. Recently, a… (more)

Subjects/Keywords: Data clustering; Alternating Projection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yang, Y. (2017). Convergence of K-indicators Clustering with Alternating Projection Algorithms. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105482

Chicago Manual of Style (16th Edition):

Yang, Yuchen. “Convergence of K-indicators Clustering with Alternating Projection Algorithms.” 2017. Masters Thesis, Rice University. Accessed May 08, 2021. http://hdl.handle.net/1911/105482.

MLA Handbook (7th Edition):

Yang, Yuchen. “Convergence of K-indicators Clustering with Alternating Projection Algorithms.” 2017. Web. 08 May 2021.

Vancouver:

Yang Y. Convergence of K-indicators Clustering with Alternating Projection Algorithms. [Internet] [Masters thesis]. Rice University; 2017. [cited 2021 May 08]. Available from: http://hdl.handle.net/1911/105482.

Council of Science Editors:

Yang Y. Convergence of K-indicators Clustering with Alternating Projection Algorithms. [Masters Thesis]. Rice University; 2017. Available from: http://hdl.handle.net/1911/105482


University of Waterloo

6. Xie, Yijun. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.

Degree: 2021, University of Waterloo

 Dimension reduction methods for functional data have been avidly studied in recent years. However, existing methods are primarily based on summarizing the data by their… (more)

Subjects/Keywords: functional data analysis; dimension reduction; projection pursuit

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Xie, Y. (2021). Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16710

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):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Thesis, University of Waterloo. Accessed May 08, 2021. http://hdl.handle.net/10012/16710.

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

MLA Handbook (7th Edition):

Xie, Yijun. “Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection.” 2021. Web. 08 May 2021.

Vancouver:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Internet] [Thesis]. University of Waterloo; 2021. [cited 2021 May 08]. Available from: http://hdl.handle.net/10012/16710.

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

Council of Science Editors:

Xie Y. Applications of Projection Pursuit in Functional Data Analysis: Goodness-of- fit, Forecasting, and Change-point Detection. [Thesis]. University of Waterloo; 2021. Available from: http://hdl.handle.net/10012/16710

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


NSYSU

7. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 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. 08 May 2021.

Vancouver:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Internet] [Thesis]. NSYSU; 2013. [cited 2021 May 08]. 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


University of California – Riverside

8. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 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. 08 May 2021.

Vancouver:

Zakaria J. Developing Efficient Algorithms for Data Mining Large Scale High Dimensional Data. [Internet] [Thesis]. University of California – Riverside; 2013. [cited 2021 May 08]. 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 Adelaide

9. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 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. 08 May 2021.

Vancouver:

Conway A. Clustering of proteomics imaging mass spectrometry data. [Internet] [Thesis]. University of Adelaide; 2016. [cited 2021 May 08]. 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


York University

10. Li, Xuan. Statistical Inference for High-Dimensional Genetic Data.

Degree: PhD, Mathematics & Statistics, 2019, York University

 This dissertation focuses on three types of high-dimensional genetic data: protein sequences, DNA methylation data, and microRNA expression data. The four major parts are presented… (more)

Subjects/Keywords: Statistics; Statistical genetics; High-dimensional data; Clustering categorical data; Model-based clustering; Two-sample problem

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Li, X. (2019). Statistical Inference for High-Dimensional Genetic Data. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/35894

Chicago Manual of Style (16th Edition):

Li, Xuan. “Statistical Inference for High-Dimensional Genetic Data.” 2019. Doctoral Dissertation, York University. Accessed May 08, 2021. http://hdl.handle.net/10315/35894.

MLA Handbook (7th Edition):

Li, Xuan. “Statistical Inference for High-Dimensional Genetic Data.” 2019. Web. 08 May 2021.

Vancouver:

Li X. Statistical Inference for High-Dimensional Genetic Data. [Internet] [Doctoral dissertation]. York University; 2019. [cited 2021 May 08]. Available from: http://hdl.handle.net/10315/35894.

Council of Science Editors:

Li X. Statistical Inference for High-Dimensional Genetic Data. [Doctoral Dissertation]. York University; 2019. Available from: http://hdl.handle.net/10315/35894


NSYSU

11. Hsu, Jen-Hao. A Study of <em class="hilite">Partialem> Periodic Utility Mining.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 The existing studies related to <em class="hilite">partialem> periodic pattern mining only consider the frequency of patterns in periodic segment data to determine their significance, and the… (more)

Subjects/Keywords: data mining; high utility; partial periodic pattern; projection; utility upper bound

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hsu, J. (2017). A Study of Partial Periodic Utility Mining. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0814117-230426

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):

Hsu, Jen-Hao. “A Study of Partial Periodic Utility Mining.” 2017. Thesis, NSYSU. Accessed May 08, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0814117-230426.

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

MLA Handbook (7th Edition):

Hsu, Jen-Hao. “A Study of Partial Periodic Utility Mining.” 2017. Web. 08 May 2021.

Vancouver:

Hsu J. A Study of Partial Periodic Utility Mining. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 May 08]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0814117-230426.

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

Council of Science Editors:

Hsu J. A Study of Partial Periodic Utility Mining. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0814117-230426

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


Texas A&M University

12. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 May 08, 2021. http://hdl.handle.net/1969.1/153224.

MLA Handbook (7th Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 08 May 2021.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 May 08]. 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


IUPUI

13. Cheung, Chung Ching. A-Optimal Subsampling For Big Data General Estimating Equations.

Degree: 2019, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

A significant hurdle for analyzing big data is the lack of effective technology and statistical inference methods. A popular approach… (more)

Subjects/Keywords: Subsampling; Big Data; A-optimality; General Estimating Equations; High Dimensional Statistics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Cheung, C. C. (2019). A-Optimal Subsampling For Big Data General Estimating Equations. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/20022

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):

Cheung, Chung Ching. “A-Optimal Subsampling For Big Data General Estimating Equations.” 2019. Thesis, IUPUI. Accessed May 08, 2021. http://hdl.handle.net/1805/20022.

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

MLA Handbook (7th Edition):

Cheung, Chung Ching. “A-Optimal Subsampling For Big Data General Estimating Equations.” 2019. Web. 08 May 2021.

Vancouver:

Cheung CC. A-Optimal Subsampling For Big Data General Estimating Equations. [Internet] [Thesis]. IUPUI; 2019. [cited 2021 May 08]. Available from: http://hdl.handle.net/1805/20022.

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

Council of Science Editors:

Cheung CC. A-Optimal Subsampling For Big Data General Estimating Equations. [Thesis]. IUPUI; 2019. Available from: http://hdl.handle.net/1805/20022

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


University of Minnesota

14. Wang, Boxiang. Modern Classification with Big Data.

Degree: PhD, Statistics, 2018, University of Minnesota

 Rapid advances in information technologies have ushered in the era of "big data" and revolutionized the scientific research across many disciplines, including economics, genomics, neuroscience,… (more)

Subjects/Keywords: Big data; Classification; High-dimensional analysis; Machine learning; Optimization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wang, B. (2018). Modern Classification with Big Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/216325

Chicago Manual of Style (16th Edition):

Wang, Boxiang. “Modern Classification with Big Data.” 2018. Doctoral Dissertation, University of Minnesota. Accessed May 08, 2021. http://hdl.handle.net/11299/216325.

MLA Handbook (7th Edition):

Wang, Boxiang. “Modern Classification with Big Data.” 2018. Web. 08 May 2021.

Vancouver:

Wang B. Modern Classification with Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2021 May 08]. Available from: http://hdl.handle.net/11299/216325.

Council of Science Editors:

Wang B. Modern Classification with Big Data. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/216325


Deakin University

15. Huynh, Viet Huu. Towards scalable Bayesian nonparametric methods for data analytics.

Degree: School of Information Technology, 2017, Deakin University

 Resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V’s): volume, variety, velocity, veracity. In this… (more)

Subjects/Keywords: big data; data mining; multi-level clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huynh, V. H. (2017). Towards scalable Bayesian nonparametric methods for data analytics. (Thesis). Deakin University. Retrieved from http://hdl.handle.net/10536/DRO/DU:30103238

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):

Huynh, Viet Huu. “Towards scalable Bayesian nonparametric methods for data analytics.” 2017. Thesis, Deakin University. Accessed May 08, 2021. http://hdl.handle.net/10536/DRO/DU:30103238.

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

MLA Handbook (7th Edition):

Huynh, Viet Huu. “Towards scalable Bayesian nonparametric methods for data analytics.” 2017. Web. 08 May 2021.

Vancouver:

Huynh VH. Towards scalable Bayesian nonparametric methods for data analytics. [Internet] [Thesis]. Deakin University; 2017. [cited 2021 May 08]. Available from: http://hdl.handle.net/10536/DRO/DU:30103238.

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

Council of Science Editors:

Huynh VH. Towards scalable Bayesian nonparametric methods for data analytics. [Thesis]. Deakin University; 2017. Available from: http://hdl.handle.net/10536/DRO/DU:30103238

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


National University of Ireland – Galway

16. Fallah, Lida. Aspects of modeling and application of survival-type data .

Degree: 2018, National University of Ireland – Galway

 Survival analysis is collection of methods for analyzing data where the outcome of interest is the time to an event and some of the observations… (more)

Subjects/Keywords: Survival analysis; Mixture models; EM algorithm; Longitudinal studies; Mixed models; High-dimensional data; Mathematics, Statistics, and Applied Mathematics; Biostatistics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Fallah, L. (2018). Aspects of modeling and application of survival-type data . (Thesis). National University of Ireland – Galway. Retrieved from http://hdl.handle.net/10379/7349

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):

Fallah, Lida. “Aspects of modeling and application of survival-type data .” 2018. Thesis, National University of Ireland – Galway. Accessed May 08, 2021. http://hdl.handle.net/10379/7349.

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

MLA Handbook (7th Edition):

Fallah, Lida. “Aspects of modeling and application of survival-type data .” 2018. Web. 08 May 2021.

Vancouver:

Fallah L. Aspects of modeling and application of survival-type data . [Internet] [Thesis]. National University of Ireland – Galway; 2018. [cited 2021 May 08]. Available from: http://hdl.handle.net/10379/7349.

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

Council of Science Editors:

Fallah L. Aspects of modeling and application of survival-type data . [Thesis]. National University of Ireland – Galway; 2018. Available from: http://hdl.handle.net/10379/7349

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

17. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 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. 08 May 2021.

Vancouver:

Freyaldenhoven S. Essays on Factor Models and Latent Variables in Economics. [Internet] [Thesis]. Brown University; 2018. [cited 2021 May 08]. 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


Purdue University

18. Cheung, Chung Ching. A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS.

Degree: Mathematics, 2019, Purdue University

  A significant hurdle for analyzing big data is the lack of effective technology and statistical inference methods. A popular approach for analyzing data with… (more)

Subjects/Keywords: Statistics; subsampling; general estimating equations; a-optimality; big data; High Dimensional Data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Cheung, C. C. (2019). A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS. (Thesis). Purdue University. Retrieved from http://hdl.handle.net/10.25394/pgs.8986571.v1

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):

Cheung, Chung Ching. “A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS.” 2019. Thesis, Purdue University. Accessed May 08, 2021. http://hdl.handle.net/10.25394/pgs.8986571.v1.

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

MLA Handbook (7th Edition):

Cheung, Chung Ching. “A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS.” 2019. Web. 08 May 2021.

Vancouver:

Cheung CC. A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS. [Internet] [Thesis]. Purdue University; 2019. [cited 2021 May 08]. Available from: http://hdl.handle.net/10.25394/pgs.8986571.v1.

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

Council of Science Editors:

Cheung CC. A-OPTIMAL SUBSAMPLING FOR BIG DATA GENERAL ESTIMATING EQUATIONS. [Thesis]. Purdue University; 2019. Available from: http://hdl.handle.net/10.25394/pgs.8986571.v1

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

19. Mohebi, Ehsan. Nonsmooth optimization models and algorithms for data clustering and visualization.

Degree: PhD, 2015, Federation University Australia

Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be… (more)

Subjects/Keywords: Cluster analysis; Clustering problems; Cluster structure; Data set; High dimensional data visualization; Algorithms; Similarity measures

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Mohebi, E. (2015). Nonsmooth optimization models and algorithms for data clustering and visualization. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/77001

Chicago Manual of Style (16th Edition):

Mohebi, Ehsan. “Nonsmooth optimization models and algorithms for data clustering and visualization.” 2015. Doctoral Dissertation, Federation University Australia. Accessed May 08, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/77001.

MLA Handbook (7th Edition):

Mohebi, Ehsan. “Nonsmooth optimization models and algorithms for data clustering and visualization.” 2015. Web. 08 May 2021.

Vancouver:

Mohebi E. Nonsmooth optimization models and algorithms for data clustering and visualization. [Internet] [Doctoral dissertation]. Federation University Australia; 2015. [cited 2021 May 08]. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/77001.

Council of Science Editors:

Mohebi E. Nonsmooth optimization models and algorithms for data clustering and visualization. [Doctoral Dissertation]. Federation University Australia; 2015. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/77001


University of Melbourne

20. Rathore, Punit. Big data cluster analysis and its applications.

Degree: 2018, University of Melbourne

 The increasing prevalence of Internet of things (IoT) technologies, smartphones, and social media services generates a huge amount of data, popularly known as ’big data’.… (more)

Subjects/Keywords: big data clustering; cluster analysis; high-dimensional data; streaming data; smart city; internet of things; online anomaly detection; online change point detection; intelligent transportation; large-scale trajectory data; trajectory prediction; scalable algorithms; single linkage, cluster validation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Rathore, P. (2018). Big data cluster analysis and its applications. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/219493

Chicago Manual of Style (16th Edition):

Rathore, Punit. “Big data cluster analysis and its applications.” 2018. Doctoral Dissertation, University of Melbourne. Accessed May 08, 2021. http://hdl.handle.net/11343/219493.

MLA Handbook (7th Edition):

Rathore, Punit. “Big data cluster analysis and its applications.” 2018. Web. 08 May 2021.

Vancouver:

Rathore P. Big data cluster analysis and its applications. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 May 08]. Available from: http://hdl.handle.net/11343/219493.

Council of Science Editors:

Rathore P. Big data cluster analysis and its applications. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/219493

21. Ahfock, Daniel Christian. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.

Degree: PhD, 2019, University of Cambridge

 This thesis is focused on the development of computationally efficient procedures for regression modelling with datasets containing a large number of observations. Standard algorithms be… (more)

Subjects/Keywords: Bayesian model selection; Random projection; Big Data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ahfock, D. C. (2019). New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.38965 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774731

Chicago Manual of Style (16th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Doctoral Dissertation, University of Cambridge. Accessed May 08, 2021. https://doi.org/10.17863/CAM.38965 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774731.

MLA Handbook (7th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Web. 08 May 2021.

Vancouver:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 May 08]. Available from: https://doi.org/10.17863/CAM.38965 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774731.

Council of Science Editors:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://doi.org/10.17863/CAM.38965 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774731


University of Cambridge

22. Ahfock, Daniel Christian. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.

Degree: PhD, 2019, University of Cambridge

 This thesis is focused on the development of computationally efficient procedures for regression modelling with datasets containing a large number of observations. Standard algorithms be… (more)

Subjects/Keywords: Bayesian model selection; Random projection; Big Data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ahfock, D. C. (2019). New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/291805https://www.repository.cam.ac.uk/bitstream/1810/291805/3/77a82354-7e80-4b67-95f4-d9b667591ca9_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/5/77a82354-7e80-4b67-95f4-d9b667591ca9.zip ; https://www.repository.cam.ac.uk/bitstream/1810/291805/6/dissertation_v2.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/7/dissertation_v2.pdf.jpg

Chicago Manual of Style (16th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Doctoral Dissertation, University of Cambridge. Accessed May 08, 2021. https://www.repository.cam.ac.uk/handle/1810/291805https://www.repository.cam.ac.uk/bitstream/1810/291805/3/77a82354-7e80-4b67-95f4-d9b667591ca9_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/5/77a82354-7e80-4b67-95f4-d9b667591ca9.zip ; https://www.repository.cam.ac.uk/bitstream/1810/291805/6/dissertation_v2.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/7/dissertation_v2.pdf.jpg.

MLA Handbook (7th Edition):

Ahfock, Daniel Christian. “New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection.” 2019. Web. 08 May 2021.

Vancouver:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 May 08]. Available from: https://www.repository.cam.ac.uk/handle/1810/291805https://www.repository.cam.ac.uk/bitstream/1810/291805/3/77a82354-7e80-4b67-95f4-d9b667591ca9_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/5/77a82354-7e80-4b67-95f4-d9b667591ca9.zip ; https://www.repository.cam.ac.uk/bitstream/1810/291805/6/dissertation_v2.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/7/dissertation_v2.pdf.jpg.

Council of Science Editors:

Ahfock DC. New statistical perspectives on efficient Big Data algorithms for high-dimensional Bayesian regression and model selection. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/291805https://www.repository.cam.ac.uk/bitstream/1810/291805/3/77a82354-7e80-4b67-95f4-d9b667591ca9_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/5/77a82354-7e80-4b67-95f4-d9b667591ca9.zip ; https://www.repository.cam.ac.uk/bitstream/1810/291805/6/dissertation_v2.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/291805/7/dissertation_v2.pdf.jpg


Virginia Tech

23. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 May 08, 2021. http://hdl.handle.net/10919/73709.

MLA Handbook (7th Edition):

Sun, Jinhui. “Robust Feature Screening Procedures for Mixed Type of Data.” 2016. Web. 08 May 2021.

Vancouver:

Sun J. Robust Feature Screening Procedures for Mixed Type of Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 May 08]. 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


Penn State University

24. Huang, Yuan. Projection Test for High-dimensional Mean Vectors with Optimal Direction.

Degree: 2015, Penn State University

 Testing the population mean is fundamental in statistical inference. When the dimensionality of a population is high, traditional Hotelling's T2 test becomes practically infeasible due… (more)

Subjects/Keywords: High-dimensional data; Hotelling's T2 test; Projection test; One-sample problem; Two-sample problem

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huang, Y. (2015). Projection Test for High-dimensional Mean Vectors with Optimal Direction. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/26249

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):

Huang, Yuan. “Projection Test for High-dimensional Mean Vectors with Optimal Direction.” 2015. Thesis, Penn State University. Accessed May 08, 2021. https://submit-etda.libraries.psu.edu/catalog/26249.

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

MLA Handbook (7th Edition):

Huang, Yuan. “Projection Test for High-dimensional Mean Vectors with Optimal Direction.” 2015. Web. 08 May 2021.

Vancouver:

Huang Y. Projection Test for High-dimensional Mean Vectors with Optimal Direction. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 May 08]. Available from: https://submit-etda.libraries.psu.edu/catalog/26249.

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

Council of Science Editors:

Huang Y. Projection Test for High-dimensional Mean Vectors with Optimal Direction. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/26249

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

25. ANTONINO, Victor Oliveira. Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas.

Degree: 2016, Federal University of Pernambuco

Métodos e algoritmos em aprendizado de máquina não supervisionado têm sido empregados em diversos problemas significativos. Uma explosão na disponibilidade de dados de várias fontes… (more)

Subjects/Keywords: Dados em Alta Dimensionalidade; Campo Receptivo Local; Aprendizagem por Relevância; Mapas Auto-Organizáveis; Agrupamento em Subespaços; High-Dimensional Data; Local Receptive Field; Relevance Learning; SelfOrganizing Maps (SOMs); Subspace Clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

ANTONINO, V. O. (2016). Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas. (Masters Thesis). Federal University of Pernambuco. Retrieved from https://repositorio.ufpe.br/handle/123456789/18623

Chicago Manual of Style (16th Edition):

ANTONINO, Victor Oliveira. “Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas.” 2016. Masters Thesis, Federal University of Pernambuco. Accessed May 08, 2021. https://repositorio.ufpe.br/handle/123456789/18623.

MLA Handbook (7th Edition):

ANTONINO, Victor Oliveira. “Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas.” 2016. Web. 08 May 2021.

Vancouver:

ANTONINO VO. Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas. [Internet] [Masters thesis]. Federal University of Pernambuco; 2016. [cited 2021 May 08]. Available from: https://repositorio.ufpe.br/handle/123456789/18623.

Council of Science Editors:

ANTONINO VO. Mapas auto-organizáveis com topologioa variante no tempo para categorização em subespaços em dados de alta dimensionalidade e vistas múltiplas. [Masters Thesis]. Federal University of Pernambuco; 2016. Available from: https://repositorio.ufpe.br/handle/123456789/18623


Duquesne University

26. Baumgardner, Adam. Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering.

Degree: MS, Computational Mathematics, 2016, Duquesne University

 A common goal in medical research is to determine the effect that a treatment has on subjects over time. Unfortunately, the analysis of data from… (more)

Subjects/Keywords: Longitudinal Data; Mixed Effects Models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Baumgardner, A. (2016). Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering. (Masters Thesis). Duquesne University. Retrieved from https://dsc.duq.edu/etd/296

Chicago Manual of Style (16th Edition):

Baumgardner, Adam. “Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering.” 2016. Masters Thesis, Duquesne University. Accessed May 08, 2021. https://dsc.duq.edu/etd/296.

MLA Handbook (7th Edition):

Baumgardner, Adam. “Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering.” 2016. Web. 08 May 2021.

Vancouver:

Baumgardner A. Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering. [Internet] [Masters thesis]. Duquesne University; 2016. [cited 2021 May 08]. Available from: https://dsc.duq.edu/etd/296.

Council of Science Editors:

Baumgardner A. Accounting for Correlation in the Analysis of Randomized Controlled Trials with Multiple Layers of Clustering. [Masters Thesis]. Duquesne University; 2016. Available from: https://dsc.duq.edu/etd/296

27. Perrot, Alexandre. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.

Degree: Docteur es, Informatique, 2017, Bordeaux

L’augmentation de la quantité de données à visualiser due au phénomène du Big Data entraîne de nouveaux défis pour le domaine de la visualisation d’information.… (more)

Subjects/Keywords: Mégadonnées; Partitionnement; Visualisation; Big Data; Clustering; Visualization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Perrot, A. (2017). La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2017BORD0775

Chicago Manual of Style (16th Edition):

Perrot, Alexandre. “La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.” 2017. Doctoral Dissertation, Bordeaux. Accessed May 08, 2021. http://www.theses.fr/2017BORD0775.

MLA Handbook (7th Edition):

Perrot, Alexandre. “La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.” 2017. Web. 08 May 2021.

Vancouver:

Perrot A. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. [Internet] [Doctoral dissertation]. Bordeaux; 2017. [cited 2021 May 08]. Available from: http://www.theses.fr/2017BORD0775.

Council of Science Editors:

Perrot A. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. [Doctoral Dissertation]. Bordeaux; 2017. Available from: http://www.theses.fr/2017BORD0775


University of Tennessee – Knoxville

28. Lu, Yuping. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.

Degree: 2019, University of Tennessee – Knoxville

 Analysis of what has come to be called “big data” presents a number of challenges as data continues to grow in size, complexity and heterogeneity.… (more)

Subjects/Keywords: big data; robustness; clustering; paraclique; outlier detection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lu, Y. (2019). Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5514

Chicago Manual of Style (16th Edition):

Lu, Yuping. “Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.” 2019. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed May 08, 2021. https://trace.tennessee.edu/utk_graddiss/5514.

MLA Handbook (7th Edition):

Lu, Yuping. “Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.” 2019. Web. 08 May 2021.

Vancouver:

Lu Y. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2019. [cited 2021 May 08]. Available from: https://trace.tennessee.edu/utk_graddiss/5514.

Council of Science Editors:

Lu Y. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_graddiss/5514


University of Minnesota

29. Traganitis, Panagiotis. Scalable and Ensemble Learning for Big Data.

Degree: PhD, Electrical/Computer Engineering, 2019, University of Minnesota

 The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices… (more)

Subjects/Keywords: Big Data; clustering; Ensemble; learning; subspace; unsupervised

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Traganitis, P. (2019). Scalable and Ensemble Learning for Big Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206358

Chicago Manual of Style (16th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Doctoral Dissertation, University of Minnesota. Accessed May 08, 2021. http://hdl.handle.net/11299/206358.

MLA Handbook (7th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Web. 08 May 2021.

Vancouver:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2021 May 08]. Available from: http://hdl.handle.net/11299/206358.

Council of Science Editors:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206358


University of Minnesota

30. Traganitis, Panagiotis. Large-scale Clustering using Random Sketching and Validation.

Degree: M.S.E.E., Electrical Engineering, 2015, University of Minnesota

 The advent of high-speed Internet, modern devices and global connectivity has introduced the world to massive amounts of data, that are being generated, communicated and… (more)

Subjects/Keywords: big data; clustering; random; sketching; SkeVa; validation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Traganitis, P. (2015). Large-scale Clustering using Random Sketching and Validation. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/175489

Chicago Manual of Style (16th Edition):

Traganitis, Panagiotis. “Large-scale Clustering using Random Sketching and Validation.” 2015. Masters Thesis, University of Minnesota. Accessed May 08, 2021. http://hdl.handle.net/11299/175489.

MLA Handbook (7th Edition):

Traganitis, Panagiotis. “Large-scale Clustering using Random Sketching and Validation.” 2015. Web. 08 May 2021.

Vancouver:

Traganitis P. Large-scale Clustering using Random Sketching and Validation. [Internet] [Masters thesis]. University of Minnesota; 2015. [cited 2021 May 08]. Available from: http://hdl.handle.net/11299/175489.

Council of Science Editors:

Traganitis P. Large-scale Clustering using Random Sketching and Validation. [Masters Thesis]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/175489

[1] [2] [3] [4] [5] … [4996]

.