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Dept: Statistics ^{❌}

You searched for `subject:( Selection)`

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Showing records 1 – 30 of
115 total matches.

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Dates

- 2015 – 2019 (46)
- 2010 – 2014 (42)
- 2005 – 2009 (27)

Universities

- Penn State University (16)
- Virginia Tech (12)
- North Carolina State University (10)

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University of Georgia

1. Zhang, Chongshan. Trending time-varying coefficient market models.

Degree: MA, Statistics, 2006, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200605_ms

► The market model, also known as single factor model or the ²-representation in Capital Asset Pricing Model (CAPM) context, is a purely statistical model used…
(more)

Subjects/Keywords: Bandwidth selection

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

APA (6^{th} Edition):

Zhang, C. (2006). Trending time-varying coefficient market models. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200605_ms

Chicago Manual of Style (16^{th} Edition):

Zhang, Chongshan. “Trending time-varying coefficient market models.” 2006. Masters Thesis, University of Georgia. Accessed October 21, 2019. http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200605_ms.

MLA Handbook (7^{th} Edition):

Zhang, Chongshan. “Trending time-varying coefficient market models.” 2006. Web. 21 Oct 2019.

Vancouver:

Zhang C. Trending time-varying coefficient market models. [Internet] [Masters thesis]. University of Georgia; 2006. [cited 2019 Oct 21]. Available from: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200605_ms.

Council of Science Editors:

Zhang C. Trending time-varying coefficient market models. [Masters Thesis]. University of Georgia; 2006. Available from: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200605_ms

Texas A&M University

2.
Shin, Minsuk.
Priors for Bayesian Shrinkage and High-Dimensional Model * Selection*.

Degree: PhD, Statistics, 2017, Texas A&M University

URL: http://hdl.handle.net/1969.1/166096

► This dissertation focuses on the choice of priors in Bayesian model *selection* and their applied, theoretical and computational aspects. As George Box famously said ?all…
(more)

Subjects/Keywords: Bayesian model selection; Nonparametric model

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

APA (6^{th} Edition):

Shin, M. (2017). Priors for Bayesian Shrinkage and High-Dimensional Model Selection. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166096

Chicago Manual of Style (16^{th} Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Doctoral Dissertation, Texas A&M University. Accessed October 21, 2019. http://hdl.handle.net/1969.1/166096.

MLA Handbook (7^{th} Edition):

Shin, Minsuk. “Priors for Bayesian Shrinkage and High-Dimensional Model Selection.” 2017. Web. 21 Oct 2019.

Vancouver:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/1969.1/166096.

Council of Science Editors:

Shin M. Priors for Bayesian Shrinkage and High-Dimensional Model Selection. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166096

University of Illinois – Urbana-Champaign

3.
Shi, Peibei.
Weak signal identification and inference in penalized model * selection*.

Degree: PhD, Statistics, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/88025

► Weak signal identification and inference are very important in the area of penalized model *selection*, yet they are under-developed and not well-studied. Existing inference procedures…
(more)

Subjects/Keywords: model selection; weak signal; inference

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

APA (6^{th} Edition):

Shi, P. (2015). Weak signal identification and inference in penalized model selection. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88025

Chicago Manual of Style (16^{th} Edition):

Shi, Peibei. “Weak signal identification and inference in penalized model selection.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 21, 2019. http://hdl.handle.net/2142/88025.

MLA Handbook (7^{th} Edition):

Shi, Peibei. “Weak signal identification and inference in penalized model selection.” 2015. Web. 21 Oct 2019.

Vancouver:

Shi P. Weak signal identification and inference in penalized model selection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/2142/88025.

Council of Science Editors:

Shi P. Weak signal identification and inference in penalized model selection. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88025

North Carolina State University

4.
Schumann, David Heinz.
Robust Variable * Selection*.

Degree: PhD, Statistics, 2009, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/4764

► The prevalence of extreme outliers in many regression data sets has led to the development of robust methods that can handle these observations. While much…
(more)

Subjects/Keywords: VAMS; outliers; variable selection; robust

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

APA (6^{th} Edition):

Schumann, D. H. (2009). Robust Variable Selection. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4764

Chicago Manual of Style (16^{th} Edition):

Schumann, David Heinz. “Robust Variable Selection.” 2009. Doctoral Dissertation, North Carolina State University. Accessed October 21, 2019. http://www.lib.ncsu.edu/resolver/1840.16/4764.

MLA Handbook (7^{th} Edition):

Schumann, David Heinz. “Robust Variable Selection.” 2009. Web. 21 Oct 2019.

Vancouver:

Schumann DH. Robust Variable Selection. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Oct 21]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4764.

Council of Science Editors:

Schumann DH. Robust Variable Selection. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4764

North Carolina State University

5.
Wu, Yujun.
Controlling Variable *Selection* By the Addition of Pseudo-Variables.

Degree: PhD, Statistics, 2004, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/5883

► Many variable *selection* procedures have been developed in the literature for linear regression models. We propose a new and general approach, the False *Selection* Rate…
(more)

Subjects/Keywords: forward selection; false selection rate; subset selection; variable selection

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

APA (6^{th} Edition):

Wu, Y. (2004). Controlling Variable Selection By the Addition of Pseudo-Variables. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5883

Chicago Manual of Style (16^{th} Edition):

Wu, Yujun. “Controlling Variable Selection By the Addition of Pseudo-Variables.” 2004. Doctoral Dissertation, North Carolina State University. Accessed October 21, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5883.

MLA Handbook (7^{th} Edition):

Wu, Yujun. “Controlling Variable Selection By the Addition of Pseudo-Variables.” 2004. Web. 21 Oct 2019.

Vancouver:

Wu Y. Controlling Variable Selection By the Addition of Pseudo-Variables. [Internet] [Doctoral dissertation]. North Carolina State University; 2004. [cited 2019 Oct 21]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5883.

Council of Science Editors:

Wu Y. Controlling Variable Selection By the Addition of Pseudo-Variables. [Doctoral Dissertation]. North Carolina State University; 2004. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5883

Arizona State University

6.
Zheng, Yi.
An Information Based Optimal Subdata *Selection* Algorithm for
Big Data Linear Regression and a Suitable Variable *Selection*
Algorithm.

Degree: Statistics, 2017, Arizona State University

URL: http://repository.asu.edu/items/44253

► This article proposes a new information-based subdata *selection* (IBOSS) algorithm, Squared Scaled Distance Algorithm (SSDA). It is based on the invariance of the determinant of…
(more)

Subjects/Keywords: Statistics; Computer science; Big Data; IBOSS; Subdata Selection; Variable Selection

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

APA (6^{th} Edition):

Zheng, Y. (2017). An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/44253

Chicago Manual of Style (16^{th} Edition):

Zheng, Yi. “An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.” 2017. Masters Thesis, Arizona State University. Accessed October 21, 2019. http://repository.asu.edu/items/44253.

MLA Handbook (7^{th} Edition):

Zheng, Yi. “An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.” 2017. Web. 21 Oct 2019.

Vancouver:

Zheng Y. An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. [Internet] [Masters thesis]. Arizona State University; 2017. [cited 2019 Oct 21]. Available from: http://repository.asu.edu/items/44253.

Council of Science Editors:

Zheng Y. An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. [Masters Thesis]. Arizona State University; 2017. Available from: http://repository.asu.edu/items/44253

North Carolina State University

7. Crews, Hugh Bates. Fast FSR Methods for Second-Order Linear Regression Models.

Degree: PhD, Statistics, 2008, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/5618

Subjects/Keywords: variable selection; regression; false selection rate; model selection

Record Details Similar Records

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

APA (6^{th} Edition):

Crews, H. B. (2008). Fast FSR Methods for Second-Order Linear Regression Models. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5618

Chicago Manual of Style (16^{th} Edition):

Crews, Hugh Bates. “Fast FSR Methods for Second-Order Linear Regression Models.” 2008. Doctoral Dissertation, North Carolina State University. Accessed October 21, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5618.

MLA Handbook (7^{th} Edition):

Crews, Hugh Bates. “Fast FSR Methods for Second-Order Linear Regression Models.” 2008. Web. 21 Oct 2019.

Vancouver:

Crews HB. Fast FSR Methods for Second-Order Linear Regression Models. [Internet] [Doctoral dissertation]. North Carolina State University; 2008. [cited 2019 Oct 21]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5618.

Council of Science Editors:

Crews HB. Fast FSR Methods for Second-Order Linear Regression Models. [Doctoral Dissertation]. North Carolina State University; 2008. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5618

Bowling Green State University

8.
Yousef, Mohammed A.
Two-Stage SCAD Lasso for Linear Mixed Model
* Selection*.

Degree: PhD, Statistics, 2019, Bowling Green State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879

► Linear regression model is the classical approach to explain the relationship between the response variable (dependent) and predictors (independent). However, when the number of predictors…
(more)

Subjects/Keywords: Statistics; Mixed model selection; SCAD Lasso; Linear mixed model; Penalized model selection; two-stage model selection

Record Details Similar Records

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

APA (6^{th} Edition):

Yousef, M. A. (2019). Two-Stage SCAD Lasso for Linear Mixed Model Selection. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879

Chicago Manual of Style (16^{th} Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Doctoral Dissertation, Bowling Green State University. Accessed October 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

MLA Handbook (7^{th} Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Web. 21 Oct 2019.

Vancouver:

Yousef MA. Two-Stage SCAD Lasso for Linear Mixed Model Selection. [Internet] [Doctoral dissertation]. Bowling Green State University; 2019. [cited 2019 Oct 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

Council of Science Editors:

Yousef MA. Two-Stage SCAD Lasso for Linear Mixed Model Selection. [Doctoral Dissertation]. Bowling Green State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879

Penn State University

9.
Yang, Tao.
An EM Based Tagging SNP *Selection* Algorithm Incorporating
Genotyping Errors.

Degree: MS, Statistics, 2014, Penn State University

URL: https://etda.libraries.psu.edu/catalog/21780

► Many tagging SNP *selection* methods depend heavily on the estimated haplotype frequencies. One limitation of the existing tagging SNP *selection* algorithms is that they assume…
(more)

Subjects/Keywords: tagging SNP selection; EM algorithm; genotyping errors

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

APA (6^{th} Edition):

Yang, T. (2014). An EM Based Tagging SNP Selection Algorithm Incorporating Genotyping Errors. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/21780

Chicago Manual of Style (16^{th} Edition):

Yang, Tao. “An EM Based Tagging SNP Selection Algorithm Incorporating Genotyping Errors.” 2014. Masters Thesis, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/21780.

MLA Handbook (7^{th} Edition):

Yang, Tao. “An EM Based Tagging SNP Selection Algorithm Incorporating Genotyping Errors.” 2014. Web. 21 Oct 2019.

Vancouver:

Yang T. An EM Based Tagging SNP Selection Algorithm Incorporating Genotyping Errors. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/21780.

Council of Science Editors:

Yang T. An EM Based Tagging SNP Selection Algorithm Incorporating Genotyping Errors. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/21780

Uppsala University

10.
PENG, SISI.
Evaluating Automatic Model * Selection*.

Degree: Statistics, 2011, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449

► In this paper, we briefly describe the automatic model *selection* which is provided by Autometrics in the PcGive program. The modeler only needs to…
(more)

Subjects/Keywords: Automatic Model Selection; Autometrics; Seasonal ARIMA; DHSY

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

APA (6^{th} Edition):

PENG, S. (2011). Evaluating Automatic Model Selection. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449

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):

PENG, SISI. “Evaluating Automatic Model Selection.” 2011. Thesis, Uppsala University. Accessed October 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

PENG, SISI. “Evaluating Automatic Model Selection.” 2011. Web. 21 Oct 2019.

Vancouver:

PENG S. Evaluating Automatic Model Selection. [Internet] [Thesis]. Uppsala University; 2011. [cited 2019 Oct 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

PENG S. Evaluating Automatic Model Selection. [Thesis]. Uppsala University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

11. Wei, Xiaoqiao. Robust combinations of statistical procedures.

Degree: PhD, Statistics, 2010, University of Minnesota

URL: http://purl.umn.edu/101344

► Alternative to model *selection*, model combination gives a combined result from the individual candidate models to share their strengths. Yang (2001, 2004) proposed square-loss-based combining…
(more)

Subjects/Keywords: Model combination; Model selection; Robust combinations; Statistics

Record Details Similar Records

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

APA (6^{th} Edition):

Wei, X. (2010). Robust combinations of statistical procedures. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/101344

Chicago Manual of Style (16^{th} Edition):

Wei, Xiaoqiao. “Robust combinations of statistical procedures.” 2010. Doctoral Dissertation, University of Minnesota. Accessed October 21, 2019. http://purl.umn.edu/101344.

MLA Handbook (7^{th} Edition):

Wei, Xiaoqiao. “Robust combinations of statistical procedures.” 2010. Web. 21 Oct 2019.

Vancouver:

Wei X. Robust combinations of statistical procedures. [Internet] [Doctoral dissertation]. University of Minnesota; 2010. [cited 2019 Oct 21]. Available from: http://purl.umn.edu/101344.

Council of Science Editors:

Wei X. Robust combinations of statistical procedures. [Doctoral Dissertation]. University of Minnesota; 2010. Available from: http://purl.umn.edu/101344

North Carolina State University

12. Shows, Justin Hall. Sparse Estimation and Inference for Censored Median Regression.

Degree: PhD, Statistics, 2009, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/5565

► Censored median regression models have been shown to be useful for analyzing a variety of censored survival data with the robustness property. We study sparse…
(more)

Subjects/Keywords: censored data; median regression; variable selection

Record Details Similar Records

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

APA (6^{th} Edition):

Shows, J. H. (2009). Sparse Estimation and Inference for Censored Median Regression. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5565

Chicago Manual of Style (16^{th} Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Doctoral Dissertation, North Carolina State University. Accessed October 21, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5565.

MLA Handbook (7^{th} Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Web. 21 Oct 2019.

Vancouver:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Oct 21]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565.

Council of Science Editors:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565

Virginia Tech

13.
Metzger, Thomas Anthony.
Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model * Selection*.

Degree: PhD, Statistics, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/93226

► Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example, recent…
(more)

Subjects/Keywords: model selection; heteroscedasticity; linear models; Bayesian

Record Details Similar Records

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

APA (6^{th} Edition):

Metzger, T. A. (2019). Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93226

Chicago Manual of Style (16^{th} Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/93226.

MLA Handbook (7^{th} Edition):

Metzger, Thomas Anthony. “Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection.” 2019. Web. 21 Oct 2019.

Vancouver:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/93226.

Council of Science Editors:

Metzger TA. Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93226

Sardar Patel University

14.
Al-Mosawi, Riyadh Rustam Muhsen.
Some contributions on estimation following
*selection*; -.

Degree: Statistics, 2010, Sardar Patel University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/34805

Subjects/Keywords: Contributions; Estimation; Estimation following selection; Selection

Record Details Similar Records

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

APA (6^{th} Edition):

Al-Mosawi, R. R. M. (2010). Some contributions on estimation following selection; -. (Thesis). Sardar Patel University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/34805

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Al-Mosawi, Riyadh Rustam Muhsen. “Some contributions on estimation following selection; -.” 2010. Thesis, Sardar Patel University. Accessed October 21, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/34805.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Al-Mosawi, Riyadh Rustam Muhsen. “Some contributions on estimation following selection; -.” 2010. Web. 21 Oct 2019.

Vancouver:

Al-Mosawi RRM. Some contributions on estimation following selection; -. [Internet] [Thesis]. Sardar Patel University; 2010. [cited 2019 Oct 21]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/34805.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Al-Mosawi RRM. Some contributions on estimation following selection; -. [Thesis]. Sardar Patel University; 2010. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/34805

Not specified: Masters Thesis or Doctoral Dissertation

The Ohio State University

15.
Yu, Lili.
Variable *selection* in the general linear model for censored
data.

Degree: PhD, Statistics, 2007, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

► Variable *selection* is a popular topic in statistics today. However, for right censored data, only a few methods are available. The principle method assumes that…
(more)

Subjects/Keywords: Statistics; LASSO; seive likelihood; model selection; right censored data

Record Details Similar Records

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

APA (6^{th} Edition):

Yu, L. (2007). Variable selection in the general linear model for censored data. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

Chicago Manual of Style (16^{th} Edition):

Yu, Lili. “Variable selection in the general linear model for censored data.” 2007. Doctoral Dissertation, The Ohio State University. Accessed October 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515.

MLA Handbook (7^{th} Edition):

Yu, Lili. “Variable selection in the general linear model for censored data.” 2007. Web. 21 Oct 2019.

Vancouver:

Yu L. Variable selection in the general linear model for censored data. [Internet] [Doctoral dissertation]. The Ohio State University; 2007. [cited 2019 Oct 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515.

Council of Science Editors:

Yu L. Variable selection in the general linear model for censored data. [Doctoral Dissertation]. The Ohio State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1173279515

The Ohio State University

16.
Bradley, Jonathan R.
* Selection* of Predictors and Estimators in Spatial
Statistics.

Degree: PhD, Statistics, 2013, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1374059401

► There are many methods for prediction and estimation available in the spatial statistics literature. Predictors (and estimators) are often derived using different assumptions on the…
(more)

Subjects/Keywords: Statistics; model selection; information criteria; spatial statistics; predictor averaging

Record Details Similar Records

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

APA (6^{th} Edition):

Bradley, J. R. (2013). Selection of Predictors and Estimators in Spatial Statistics. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1374059401

Chicago Manual of Style (16^{th} Edition):

Bradley, Jonathan R. “Selection of Predictors and Estimators in Spatial Statistics.” 2013. Doctoral Dissertation, The Ohio State University. Accessed October 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374059401.

MLA Handbook (7^{th} Edition):

Bradley, Jonathan R. “Selection of Predictors and Estimators in Spatial Statistics.” 2013. Web. 21 Oct 2019.

Vancouver:

Bradley JR. Selection of Predictors and Estimators in Spatial Statistics. [Internet] [Doctoral dissertation]. The Ohio State University; 2013. [cited 2019 Oct 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1374059401.

Council of Science Editors:

Bradley JR. Selection of Predictors and Estimators in Spatial Statistics. [Doctoral Dissertation]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1374059401

Oregon State University

17.
Usner, Dale Wesley.
Persistence and heterogeneity in habitat *selection* studies.

Degree: PhD, Statistics, 2000, Oregon State University

URL: http://hdl.handle.net/1957/32513

► Recently the independent multinomial selections model (IMS) with the multinomial logit link has been suggested as an analysis tool for radio-telemetry habitat *selection* data. This…
(more)

Subjects/Keywords: Habitat selection – Statistical methods

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

APA (6^{th} Edition):

Usner, D. W. (2000). Persistence and heterogeneity in habitat selection studies. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/32513

Chicago Manual of Style (16^{th} Edition):

Usner, Dale Wesley. “Persistence and heterogeneity in habitat selection studies.” 2000. Doctoral Dissertation, Oregon State University. Accessed October 21, 2019. http://hdl.handle.net/1957/32513.

MLA Handbook (7^{th} Edition):

Usner, Dale Wesley. “Persistence and heterogeneity in habitat selection studies.” 2000. Web. 21 Oct 2019.

Vancouver:

Usner DW. Persistence and heterogeneity in habitat selection studies. [Internet] [Doctoral dissertation]. Oregon State University; 2000. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/1957/32513.

Council of Science Editors:

Usner DW. Persistence and heterogeneity in habitat selection studies. [Doctoral Dissertation]. Oregon State University; 2000. Available from: http://hdl.handle.net/1957/32513

Bowling Green State University

18.
Pan, Juming.
Adaptive LASSO For Mixed Model *Selection* via Profile
Log-Likelihood.

Degree: PhD, Statistics, 2016, Bowling Green State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921

► Linear mixed models describe the relationship between a response variable and some predictors for data that are grouped according to one or more clustering factors.…
(more)

Subjects/Keywords: Statistics; model selection; linear mixed models; oracle properties

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

APA (6^{th} Edition):

Pan, J. (2016). Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921

Chicago Manual of Style (16^{th} Edition):

Pan, Juming. “Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood.” 2016. Doctoral Dissertation, Bowling Green State University. Accessed October 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.

MLA Handbook (7^{th} Edition):

Pan, Juming. “Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood.” 2016. Web. 21 Oct 2019.

Vancouver:

Pan J. Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood. [Internet] [Doctoral dissertation]. Bowling Green State University; 2016. [cited 2019 Oct 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.

Council of Science Editors:

Pan J. Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood. [Doctoral Dissertation]. Bowling Green State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921

Penn State University

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

Degree: PhD, Statistics, 2011, Penn State University

URL: https://etda.libraries.psu.edu/catalog/12143

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

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

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

APA (6^{th} Edition):

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

Chicago Manual of Style (16^{th} Edition):

Li, Jiahan. “THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES.” 2011. Doctoral Dissertation, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/12143.

MLA Handbook (7^{th} Edition):

Li, Jiahan. “THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES.” 2011. Web. 21 Oct 2019.

Vancouver:

Li J. THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/12143.

Council of Science Editors:

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

Penn State University

20.
Zhong, Wei.
feature screening and variable *selection* for ultrahigh
dimensional data analysis.

Degree: PhD, Statistics, 2012, Penn State University

URL: https://etda.libraries.psu.edu/catalog/14917

► This dissertation is concerned with feature screening and variable *selection* in ultrahigh dimensional data analysis, where the number of predictors, p, greatly exceeds the sample…
(more)

Subjects/Keywords: ultrahigh dimensionality; distance correlation; feature screening; sure screening property; variable selection

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

APA (6^{th} Edition):

Zhong, W. (2012). feature screening and variable selection for ultrahigh dimensional data analysis. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14917

Chicago Manual of Style (16^{th} Edition):

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Doctoral Dissertation, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/14917.

MLA Handbook (7^{th} Edition):

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Web. 21 Oct 2019.

Vancouver:

Zhong W. feature screening and variable selection for ultrahigh dimensional data analysis. [Internet] [Doctoral dissertation]. Penn State University; 2012. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/14917.

Council of Science Editors:

Zhong W. feature screening and variable selection for ultrahigh dimensional data analysis. [Doctoral Dissertation]. Penn State University; 2012. Available from: https://etda.libraries.psu.edu/catalog/14917

Penn State University

21.
Kai, Bo.
Variable *Selection* in Robust Linear Models.

Degree: MS, Statistics, 2008, Penn State University

URL: https://etda.libraries.psu.edu/catalog/8276

► Variable *selection* plays very important roles in statistical learning. Traditional stepwise subset *selection* methods are widely used in practice, but they are difficult to implement…
(more)

Subjects/Keywords: robust regression; linear models; variable selection; SS penalty; oracle property

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

APA (6^{th} Edition):

Kai, B. (2008). Variable Selection in Robust Linear Models. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8276

Chicago Manual of Style (16^{th} Edition):

Kai, Bo. “Variable Selection in Robust Linear Models.” 2008. Masters Thesis, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/8276.

MLA Handbook (7^{th} Edition):

Kai, Bo. “Variable Selection in Robust Linear Models.” 2008. Web. 21 Oct 2019.

Vancouver:

Kai B. Variable Selection in Robust Linear Models. [Internet] [Masters thesis]. Penn State University; 2008. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/8276.

Council of Science Editors:

Kai B. Variable Selection in Robust Linear Models. [Masters Thesis]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8276

Penn State University

22. Chung, Yeojin. LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING.

Degree: PhD, Statistics, 2010, Penn State University

URL: https://etda.libraries.psu.edu/catalog/11149

► Nonparametric density estimation is widely used for investigating underlying features of data. We introduce a likelihood enhanced nonparametric density estimator which arises from treating the…
(more)

Subjects/Keywords: Nonparametric Density Estimation; Nonparametric Maximum Likelihood; Clustering; Bandwidth Selection; Nonparametric Mixture

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

APA (6^{th} Edition):

Chung, Y. (2010). LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11149

Chicago Manual of Style (16^{th} Edition):

Chung, Yeojin. “LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING.” 2010. Doctoral Dissertation, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/11149.

MLA Handbook (7^{th} Edition):

Chung, Yeojin. “LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING.” 2010. Web. 21 Oct 2019.

Vancouver:

Chung Y. LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING. [Internet] [Doctoral dissertation]. Penn State University; 2010. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/11149.

Council of Science Editors:

Chung Y. LIKELIHOOD-TUNED DENSITY ESTIMATOR AND ITS APPLICATION TO CLUSTERING. [Doctoral Dissertation]. Penn State University; 2010. Available from: https://etda.libraries.psu.edu/catalog/11149

Penn State University

23. Pittman, Jennifer Lynn. ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING.

Degree: PhD, Statistics, 2000, Penn State University

URL: https://etda.libraries.psu.edu/catalog/5815

► Many statistical analyses and applications require the capture of a relationship between two variables X and Y that is more complex than a simple linear…
(more)

Subjects/Keywords: model selection; evolutionary computation; splines

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APA (6^{th} Edition):

Pittman, J. L. (2000). ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/5815

Chicago Manual of Style (16^{th} Edition):

Pittman, Jennifer Lynn. “ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING.” 2000. Doctoral Dissertation, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/5815.

MLA Handbook (7^{th} Edition):

Pittman, Jennifer Lynn. “ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING.” 2000. Web. 21 Oct 2019.

Vancouver:

Pittman JL. ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING. [Internet] [Doctoral dissertation]. Penn State University; 2000. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/5815.

Council of Science Editors:

Pittman JL. ADAPTIVE SPLINES AND GENETIC ALGORITHMS FOR OPTIMAL STATISTICAL MODELING. [Doctoral Dissertation]. Penn State University; 2000. Available from: https://etda.libraries.psu.edu/catalog/5815

Penn State University

24.
Lou, Lejia.
Thresholded partial correlation approach for variable
*selection* in linear models and partially linear models.

Degree: PhD, Statistics, 2013, Penn State University

URL: https://etda.libraries.psu.edu/catalog/19634

► This thesis is concerned with variable *selection* in linear models and partially linear models for high-dimensional data analysis. With the development of technology, it is…
(more)

Subjects/Keywords: Variable Selection; Linear Model; Partially Linear Model; Nonparametric Regression

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

APA (6^{th} Edition):

Lou, L. (2013). Thresholded partial correlation approach for variable selection in linear models and partially linear models. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/19634

Chicago Manual of Style (16^{th} Edition):

Lou, Lejia. “Thresholded partial correlation approach for variable selection in linear models and partially linear models.” 2013. Doctoral Dissertation, Penn State University. Accessed October 21, 2019. https://etda.libraries.psu.edu/catalog/19634.

MLA Handbook (7^{th} Edition):

Lou, Lejia. “Thresholded partial correlation approach for variable selection in linear models and partially linear models.” 2013. Web. 21 Oct 2019.

Vancouver:

Lou L. Thresholded partial correlation approach for variable selection in linear models and partially linear models. [Internet] [Doctoral dissertation]. Penn State University; 2013. [cited 2019 Oct 21]. Available from: https://etda.libraries.psu.edu/catalog/19634.

Council of Science Editors:

Lou L. Thresholded partial correlation approach for variable selection in linear models and partially linear models. [Doctoral Dissertation]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/19634

Bowling Green State University

25.
Liu, Yang.
Improving the Accuracy of Variable *Selection* Using the Whole
Solution Path.

Degree: PhD, Statistics, 2015, Bowling Green State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

► The performances of penalized least squares approaches profoundly depend on the *selection* of the tuning parameter; however, statisticians did not reach consensus on the criterion…
(more)

Subjects/Keywords: Statistics; variable selection; high dimensional data; SPSP; AIS

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

APA (6^{th} Edition):

Liu, Y. (2015). Improving the Accuracy of Variable Selection Using the Whole Solution Path. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

Chicago Manual of Style (16^{th} Edition):

Liu, Yang. “Improving the Accuracy of Variable Selection Using the Whole Solution Path.” 2015. Doctoral Dissertation, Bowling Green State University. Accessed October 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170.

MLA Handbook (7^{th} Edition):

Liu, Yang. “Improving the Accuracy of Variable Selection Using the Whole Solution Path.” 2015. Web. 21 Oct 2019.

Vancouver:

Liu Y. Improving the Accuracy of Variable Selection Using the Whole Solution Path. [Internet] [Doctoral dissertation]. Bowling Green State University; 2015. [cited 2019 Oct 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170.

Council of Science Editors:

Liu Y. Improving the Accuracy of Variable Selection Using the Whole Solution Path. [Doctoral Dissertation]. Bowling Green State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435858170

Virginia Tech

26. Loftus, Stephen Christopher. On the Use of Grouped Covariate Regression in Oversaturated Models.

Degree: PhD, Statistics, 2015, Virginia Tech

URL: http://hdl.handle.net/10919/64363

► As data collection techniques improve, oftentimes the number of covariates exceeds the number of observations. When this happens, regression models become oversaturated and, thus, inestimable.…
(more)

Subjects/Keywords: Oversaturated model; Big data; Variable selection; Data Analytics; Bayesian methods

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

APA (6^{th} Edition):

Loftus, S. C. (2015). On the Use of Grouped Covariate Regression in Oversaturated Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64363

Chicago Manual of Style (16^{th} Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/64363.

MLA Handbook (7^{th} Edition):

Loftus, Stephen Christopher. “On the Use of Grouped Covariate Regression in Oversaturated Models.” 2015. Web. 21 Oct 2019.

Vancouver:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/64363.

Council of Science Editors:

Loftus SC. On the Use of Grouped Covariate Regression in Oversaturated Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64363

Virginia Tech

27.
Velasco-Cruz, Ciro.
Spatially Correlated Model *Selection* (SCOMS).

Degree: PhD, Statistics, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/27791

► In this dissertation, a variable *selection* method for spatial data is developed. It is assumed that the spatial process is non-stationary as a whole but…
(more)

Subjects/Keywords: Spatial statistics; Ising prior; Non-stationary spatial fields; Variable Selection

Record Details Similar Records

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

APA (6^{th} Edition):

Velasco-Cruz, C. (2012). Spatially Correlated Model Selection (SCOMS). (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/27791

Chicago Manual of Style (16^{th} Edition):

Velasco-Cruz, Ciro. “Spatially Correlated Model Selection (SCOMS).” 2012. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/27791.

MLA Handbook (7^{th} Edition):

Velasco-Cruz, Ciro. “Spatially Correlated Model Selection (SCOMS).” 2012. Web. 21 Oct 2019.

Vancouver:

Velasco-Cruz C. Spatially Correlated Model Selection (SCOMS). [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/27791.

Council of Science Editors:

Velasco-Cruz C. Spatially Correlated Model Selection (SCOMS). [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/27791

Virginia Tech

28.
Maiti, Dipayan.
Multiset Model *Selection* and Averaging, and Interactive Storytelling.

Degree: PhD, Statistics, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/28563

► The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and…
(more)

Subjects/Keywords: supervised topic modeling; visual analytics; bayesian model averaging; Bayesian mode selection

Record Details Similar Records

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

APA (6^{th} Edition):

Maiti, D. (2012). Multiset Model Selection and Averaging, and Interactive Storytelling. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28563

Chicago Manual of Style (16^{th} Edition):

Maiti, Dipayan. “Multiset Model Selection and Averaging, and Interactive Storytelling.” 2012. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/28563.

MLA Handbook (7^{th} Edition):

Maiti, Dipayan. “Multiset Model Selection and Averaging, and Interactive Storytelling.” 2012. Web. 21 Oct 2019.

Vancouver:

Maiti D. Multiset Model Selection and Averaging, and Interactive Storytelling. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/28563.

Council of Science Editors:

Maiti D. Multiset Model Selection and Averaging, and Interactive Storytelling. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28563

Virginia Tech

29.
Xie, Yimeng.
Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable * Selection*.

Degree: PhD, Statistics, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/71687

► This dissertation focuses on three research projects: 1) construction of simultaneous prediction intervals/bounds for at least k out of m future observations; 2) semi-parametric degradation…
(more)

Subjects/Keywords: ADDT; Degradation Model; Spatial Variable Selection; SPI/SPB

Record Details Similar Records

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

APA (6^{th} Edition):

Xie, Y. (2016). Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71687

Chicago Manual of Style (16^{th} Edition):

Xie, Yimeng. “Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/71687.

MLA Handbook (7^{th} Edition):

Xie, Yimeng. “Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection.” 2016. Web. 21 Oct 2019.

Vancouver:

Xie Y. Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. Available from: http://hdl.handle.net/10919/71687.

Council of Science Editors:

Xie Y. Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71687

Virginia Tech

30. 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

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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 October 21, 2019. http://hdl.handle.net/10919/73709.

MLA Handbook (7^{th} Edition):

Sun, Jinhui. “Robust Feature Screening Procedures for Mixed Type of Data.” 2016. Web. 21 Oct 2019.

Vancouver:

Sun J. Robust Feature Screening Procedures for Mixed Type of Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 21]. 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