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Dept: Statistics

You searched for subject:( Selection). Showing records 1 – 30 of 115 total matches.

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University of Georgia

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

Degree: MA, Statistics, 2006, University of Georgia

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

None

Bibliography given

Advisors/Committee Members: Shanubhogue, Ashok.

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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Sun, Jinhui. “Robust Feature Screening Procedures for Mixed Type of Data.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 21, 2019. http://hdl.handle.net/10919/73709.

MLA Handbook (7th 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

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