Advanced search options

Sorted by: relevance · author · university · date | New 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

Dates

- 2017 – 2021 (42556)
- 2012 – 2016 (58811)
- 2007 – 2011 (32859)
- 2002 – 2006 (12109)
- 1997 – 2001 (4429)
- 1992 – 1996 (2507)
- 1987 – 1991 (1942)
- 1982 – 1986 (1538)
- 1977 – 1981 (977)
- 1972 – 1976 (775)

Universities

- Brazil (8174)
- University of São Paulo (6367)
- Brno University of Technology (3729)
- Repositório Científico Lusófona (3496)
- KTH (2529)
- Universidade Estadual de Campinas (2409)
- ETH Zürich (2350)
- Universidade Estadual Paulista (UNESP) (2000)
- University of Michigan (1921)
- RCAAP (1666)
- Universidade Federal de Minas Gerais; UFMG (1623)
- University of Florida (1606)
- Virginia Tech (1509)
- Delft University of Technology (1436)
- Texas A&M University (1309)

Department

- Computer Science (1840)
- Electrical Engineering and Computer Science (EECS) (1376)
- Electrical and Computer Engineering (1305)
- Electrical Engineering (1257)
- Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers) (1007)
- Mechanical Engineering (980)
- Education (881)
- Informatique (861)
- Physics (831)
- Statistics (734)
- Civil Engineering (693)
- Psychology (625)
- Information and Communication Technology (ICT) (525)
- Chemistry (491)
- Mathematics (489)

Degrees

- PhD (20372)
- MS (7439)
- Docteur es (5790)
- Master (1802)
- MA (1150)
- EdD (805)
- MEd (241)
- MS(M.S.) (217)
- MSin Engineering (216)
- MSc (194)
- Doctor of Education (EdD) (181)
- MFA (155)
- MSEd (148)
- M Ed (129)
- M.F.A. (123)

Levels

- doctoral (46165)
- masters (42308)
- thesis (1725)
- dissertation (191)
- project (135)
- doctor of philosophy ph.d. (112)
- doctor of philosophy (ph.d.) (72)
- thesis - unrestricted (61)
- dissertation - unrestricted (45)
- open access (33)

Languages

Country

- US (42189)
- Brazil (41123)
- Portugal (9075)
- Sweden (9075)
- Canada (6450)
- France (5791)
- UK (4103)
- Australia (4062)
- Czech Republic (3790)
- Netherlands (3229)
- South Africa (2578)
- Switzerland (2375)
- Greece (2081)
- Hong Kong (2075)
- New Zealand (1630)

▼ Search Limiters

1.
Cho, Jang Ik.
<*em* class="hilite"><*em* class="hilite">Partialem>em> *EM* Procedure for *Big*-* Data* Linear

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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/

►

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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/199089

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-151378

►

<*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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

URL: http://hdl.handle.net/1911/105482

► *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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10012/16710

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Riverside

8.
Zakaria, Jesin.
Developing Efficient Algorithms for * Data* Mining Large Scale

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

URL: http://www.escholarship.org/uc/item/660316zp

► *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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

University of Adelaide

9.
Conway, Annie.
* Clustering* of proteomics imaging mass spectrometry

Degree: 2016, University of Adelaide

URL: http://hdl.handle.net/2440/112036

► 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 Details Similar Records

❌

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10315/35894

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0814117-230426

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/1969.1/153224

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1805/20022

►

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 Details Similar Records

❌

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/11299/216325

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10536/DRO/DU:30103238

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10379/7349

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://repository.library.brown.edu/studio/item/bdr:792643/

► 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 Details Similar Records

❌

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

APA (6^{th} 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/

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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/.

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/

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

URL: http://hdl.handle.net/10.25394/pgs.8986571.v1

► 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 Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/77001

►

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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11343/219493

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: PhD, 2019, University of Cambridge

URL: https://doi.org/10.17863/CAM.38965 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774731

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: PhD, 2019, University of Cambridge

URL: 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

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/10919/73709

► *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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/26249

► Testing the population mean is fundamental in statistical inference. When the dimensionality of a population is *high*, traditional Hotelling's T^{2} test becomes practically infeasible due…
(more)

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

Record Details Similar Records

❌

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://repositorio.ufpe.br/handle/123456789/18623

►

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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://dsc.duq.edu/etd/296

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: Docteur es, Informatique, 2017, Bordeaux

URL: http://www.theses.fr/2017BORD0775

►

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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: 2019, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_graddiss/5514

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/206358

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/11299/175489

► 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 Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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