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Showing records 1 – 30 of
415 total matches.

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- 2017 – 2021 (136)
- 2012 – 2016 (180)
- 2007 – 2011 (94)
- 2002 – 2006 (16)

Universities

- Brazil (17)
- Penn State University (15)
- Texas A&M University (13)
- Georgia Tech (12)
- National University of Singapore (10)
- Rice University (10)

Department

- Statistics (61)
- Biostatistics (14)
- Industrial and Systems Engineering (13)
- Engineering (10)

Degrees

- PhD (163)
- Docteur es (33)
- MS (19)

Languages

- English (223)
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University of Manchester

1.
Nogueira, Sarah.
Quantifying the Stability of Feature * Selection*.

Degree: 2018, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

► Feature *Selection* is central to modern data science, from exploratory data analysis to predictive model-building. The "stability"of a feature *selection* algorithm refers to the robustness…
(more)

Subjects/Keywords: Stability; Feature Selection; 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):

Nogueira, S. (2018). Quantifying the Stability of Feature Selection. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

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

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Doctoral Dissertation, University of Manchester. Accessed April 12, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

MLA Handbook (7^{th} Edition):

Nogueira, Sarah. “Quantifying the Stability of Feature Selection.” 2018. Web. 12 Apr 2021.

Vancouver:

Nogueira S. Quantifying the Stability of Feature Selection. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2021 Apr 12]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287.

Council of Science Editors:

Nogueira S. Quantifying the Stability of Feature Selection. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313287

North Carolina State University

2.
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 April 12, 2021. http://www.lib.ncsu.edu/resolver/1840.16/4764.

MLA Handbook (7^{th} Edition):

Schumann, David Heinz. “Robust Variable Selection.” 2009. Web. 12 Apr 2021.

Vancouver:

Schumann DH. Robust Variable Selection. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2021 Apr 12]. 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

Rice University

3. Shoemaker, Katherine. Statistical Approaches for Interpretable Radiomics.

Degree: PhD, Engineering, 2019, Rice University

URL: http://hdl.handle.net/1911/106005

► Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The emerging field of radiomics aims to extract quantitative features…
(more)

Subjects/Keywords: radiomics; Bayesian; trees; variable selection

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

APA (6^{th} Edition):

Shoemaker, K. (2019). Statistical Approaches for Interpretable Radiomics. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/106005

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

Shoemaker, Katherine. “Statistical Approaches for Interpretable Radiomics.” 2019. Doctoral Dissertation, Rice University. Accessed April 12, 2021. http://hdl.handle.net/1911/106005.

MLA Handbook (7^{th} Edition):

Shoemaker, Katherine. “Statistical Approaches for Interpretable Radiomics.” 2019. Web. 12 Apr 2021.

Vancouver:

Shoemaker K. Statistical Approaches for Interpretable Radiomics. [Internet] [Doctoral dissertation]. Rice University; 2019. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1911/106005.

Council of Science Editors:

Shoemaker K. Statistical Approaches for Interpretable Radiomics. [Doctoral Dissertation]. Rice University; 2019. Available from: http://hdl.handle.net/1911/106005

University of Saskatchewan

4. Dong, Yue. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.

Degree: 2016, University of Saskatchewan

URL: http://hdl.handle.net/10388/7313

► When modelling count data, it is possible to have excessive zeros in the data in many applications. My thesis concentrates on the *variable* *selection* in…
(more)

Subjects/Keywords: Variable selection; Zero-inflated model; Bayesian LASSO

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

APA (6^{th} Edition):

Dong, Y. (2016). A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7313

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):

Dong, Yue. “A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.” 2016. Thesis, University of Saskatchewan. Accessed April 12, 2021. http://hdl.handle.net/10388/7313.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Dong, Yue. “A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models.” 2016. Web. 12 Apr 2021.

Vancouver:

Dong Y. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. [Internet] [Thesis]. University of Saskatchewan; 2016. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10388/7313.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dong Y. A Simulation Study to Evaluate Bayesian LASSO’s Performance in Zero-Inflated Poisson (ZIP) Models. [Thesis]. University of Saskatchewan; 2016. Available from: http://hdl.handle.net/10388/7313

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

5. Yang, Ching Chi. Dimensional Analysis for Response Surface Methodology.

Degree: 2019, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/16110cuy130

► Dimensional analysis is a widely-employed methodology in physics and engineering. Its advantages include, but not limited to: (i) the essential information extraction, (ii) the interpretability…
(more)

Subjects/Keywords: Dimension reduction; Function approximation; Optimization; Variable selection

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APA (6^{th} Edition):

Yang, C. C. (2019). Dimensional Analysis for Response Surface Methodology. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16110cuy130

Not specified: Masters Thesis or Doctoral Dissertation

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

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yang, Ching Chi. “Dimensional Analysis for Response Surface Methodology.” 2019. Web. 12 Apr 2021.

Vancouver:

Yang CC. Dimensional Analysis for Response Surface Methodology. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/16110cuy130.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang CC. Dimensional Analysis for Response Surface Methodology. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16110cuy130

Not specified: Masters Thesis or Doctoral Dissertation

North Carolina State University

6. 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 April 12, 2021. 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. 12 Apr 2021.

Vancouver:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2021 Apr 12]. 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

University of Illinois – Chicago

7. Sun, Yan. A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria.

Degree: 2015, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/19777

► Subgroup identification has always been of great interest among the many functions and applications of statistical learning. In the pharmaceutical area, it is desirable to…
(more)

Subjects/Keywords: subgroup; personalized medicine; variable selection; quantitative criteria

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

APA (6^{th} Edition):

Sun, Y. (2015). A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19777

Not specified: Masters Thesis or Doctoral Dissertation

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

Sun, Yan. “A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria.” 2015. Thesis, University of Illinois – Chicago. Accessed April 12, 2021. http://hdl.handle.net/10027/19777.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sun, Yan. “A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria.” 2015. Web. 12 Apr 2021.

Vancouver:

Sun Y. A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria. [Internet] [Thesis]. University of Illinois – Chicago; 2015. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10027/19777.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun Y. A Subgroup Identification Method with Interaction Filtering and Quantitative Criteria. [Thesis]. University of Illinois – Chicago; 2015. Available from: http://hdl.handle.net/10027/19777

Not specified: Masters Thesis or Doctoral Dissertation

8.
Rockova, Veronika.
Bayesian *Variable* *Selection* in High-dimensional Applications.

Degree: 2013, Erasmus University Medical Center

URL: http://hdl.handle.net/1765/51587

► markdownabstract__Abstract__ Advances in research technologies over the past few decades have encouraged the proliferation of massive datasets, revolutionizing statistical perspectives on high-dimensionality. Highthroughput technologies have…
(more)

Subjects/Keywords: Bayesian 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):

Rockova, V. (2013). Bayesian Variable Selection in High-dimensional Applications. (Doctoral Dissertation). Erasmus University Medical Center. Retrieved from http://hdl.handle.net/1765/51587

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

Rockova, Veronika. “Bayesian Variable Selection in High-dimensional Applications.” 2013. Doctoral Dissertation, Erasmus University Medical Center. Accessed April 12, 2021. http://hdl.handle.net/1765/51587.

MLA Handbook (7^{th} Edition):

Rockova, Veronika. “Bayesian Variable Selection in High-dimensional Applications.” 2013. Web. 12 Apr 2021.

Vancouver:

Rockova V. Bayesian Variable Selection in High-dimensional Applications. [Internet] [Doctoral dissertation]. Erasmus University Medical Center; 2013. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1765/51587.

Council of Science Editors:

Rockova V. Bayesian Variable Selection in High-dimensional Applications. [Doctoral Dissertation]. Erasmus University Medical Center; 2013. Available from: http://hdl.handle.net/1765/51587

University of Montana

9. Burbach, Thor. THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST.

Degree: MS, 2011, University of Montana

URL: https://scholarworks.umt.edu/etd/1028

► Habitat suitability modeling is widely-used in both biogeography and ecology to characterize the biophysical requirements and distribution of plant and animal species. Many of…
(more)

Subjects/Keywords: GIS; Habitat modeling; Rare plant; 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):

Burbach, T. (2011). THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST. (Masters Thesis). University of Montana. Retrieved from https://scholarworks.umt.edu/etd/1028

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

Burbach, Thor. “THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST.” 2011. Masters Thesis, University of Montana. Accessed April 12, 2021. https://scholarworks.umt.edu/etd/1028.

MLA Handbook (7^{th} Edition):

Burbach, Thor. “THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST.” 2011. Web. 12 Apr 2021.

Vancouver:

Burbach T. THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST. [Internet] [Masters thesis]. University of Montana; 2011. [cited 2021 Apr 12]. Available from: https://scholarworks.umt.edu/etd/1028.

Council of Science Editors:

Burbach T. THE INFLUENCE OF ENVIRONMENTAL VARIABLES ON PREDICTING RARE-PLANT HABITAT IN THE NEZ PERCE NATIONAL FOREST. [Masters Thesis]. University of Montana; 2011. Available from: https://scholarworks.umt.edu/etd/1028

10.
HU XIAOLI.
Subset *selection* in regression model.

Degree: 2007, National University of Singapore

URL: https://scholarbank.nus.edu.sg/handle/10635/16159

Subjects/Keywords: LASSO; 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):

XIAOLI, H. (2007). Subset selection in regression model. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/16159

Not specified: Masters Thesis or Doctoral Dissertation

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

XIAOLI, HU. “Subset selection in regression model.” 2007. Thesis, National University of Singapore. Accessed April 12, 2021. https://scholarbank.nus.edu.sg/handle/10635/16159.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

XIAOLI, HU. “Subset selection in regression model.” 2007. Web. 12 Apr 2021.

Vancouver:

XIAOLI H. Subset selection in regression model. [Internet] [Thesis]. National University of Singapore; 2007. [cited 2021 Apr 12]. Available from: https://scholarbank.nus.edu.sg/handle/10635/16159.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

XIAOLI H. Subset selection in regression model. [Thesis]. National University of Singapore; 2007. Available from: https://scholarbank.nus.edu.sg/handle/10635/16159

Not specified: Masters Thesis or Doctoral Dissertation

Rice University

11.
Kim, Soyeon.
Prediction Oriented Marker *Selection* (PROMISE) for High Dimensional Regression with Application to Personalized Medicine.

Degree: PhD, Engineering, 2015, Rice University

URL: http://hdl.handle.net/1911/88443

► In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on a patients individual biomarker profile. Two important goals…
(more)

Subjects/Keywords: Predictive marker; Personalized medicine; Cross-validation; Stability Selection; Variable Selection; Lasso

Record Details Similar Records

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

APA (6^{th} Edition):

Kim, S. (2015). Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/88443

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

Kim, Soyeon. “Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine.” 2015. Doctoral Dissertation, Rice University. Accessed April 12, 2021. http://hdl.handle.net/1911/88443.

MLA Handbook (7^{th} Edition):

Kim, Soyeon. “Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine.” 2015. Web. 12 Apr 2021.

Vancouver:

Kim S. Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine. [Internet] [Doctoral dissertation]. Rice University; 2015. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1911/88443.

Council of Science Editors:

Kim S. Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine. [Doctoral Dissertation]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88443

Arizona State University

12.
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

Record Details Similar Records

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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 April 12, 2021. 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. 12 Apr 2021.

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 2021 Apr 12]. 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

Texas A&M University

13.
Xue, Jingnan.
Robust Model-free *Variable* Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.

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

URL: http://hdl.handle.net/1969.1/187253

► Big data analysis and high dimensional data analysis are two popular and challenging topics in current statistical research. They bring us a lot of opportunities…
(more)

Subjects/Keywords: Variable selection; variable screening; ultrahigh dimensional data analysis; big data; parallel computing; MCMC

Record Details Similar Records

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

APA (6^{th} Edition):

Xue, J. (2017). Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/187253

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

Xue, Jingnan. “Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.” 2017. Doctoral Dissertation, Texas A&M University. Accessed April 12, 2021. http://hdl.handle.net/1969.1/187253.

MLA Handbook (7^{th} Edition):

Xue, Jingnan. “Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion.” 2017. Web. 12 Apr 2021.

Vancouver:

Xue J. Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1969.1/187253.

Council of Science Editors:

Xue J. Robust Model-free Variable Screening, Double-parallel Monte Carlo and Average Bayesian Information Criterion. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/187253

14. Kahmann, Alessandro. Seleção de variáveis para classificação de bateladas produtivas.

Degree: 2013, Brazil

URL: http://hdl.handle.net/10183/96394

►

Bancos de dados oriundos de processos industriais são caracterizados por elevado número de variáveis correlacionadas, dados ruidosos e maior número de variáveis do que observações,… (more)

Subjects/Keywords: Métodos estatísticos; Análise multivariada; Gestão da produção; Variable selection; Variable importance index; Classification

Record Details Similar Records

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

APA (6^{th} Edition):

Kahmann, A. (2013). Seleção de variáveis para classificação de bateladas produtivas. (Masters Thesis). Brazil. Retrieved from http://hdl.handle.net/10183/96394

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

Kahmann, Alessandro. “Seleção de variáveis para classificação de bateladas produtivas.” 2013. Masters Thesis, Brazil. Accessed April 12, 2021. http://hdl.handle.net/10183/96394.

MLA Handbook (7^{th} Edition):

Kahmann, Alessandro. “Seleção de variáveis para classificação de bateladas produtivas.” 2013. Web. 12 Apr 2021.

Vancouver:

Kahmann A. Seleção de variáveis para classificação de bateladas produtivas. [Internet] [Masters thesis]. Brazil; 2013. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10183/96394.

Council of Science Editors:

Kahmann A. Seleção de variáveis para classificação de bateladas produtivas. [Masters Thesis]. Brazil; 2013. Available from: http://hdl.handle.net/10183/96394

North Carolina State University

15.
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

Record Details Similar Records

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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 April 12, 2021. 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. 12 Apr 2021.

Vancouver:

Wu Y. Controlling Variable Selection By the Addition of Pseudo-Variables. [Internet] [Doctoral dissertation]. North Carolina State University; 2004. [cited 2021 Apr 12]. 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

KTH

16.
Hjerpe, Adam.
Computing Random Forests *Variable* Importance Measures (VIM) on Mixed Numerical and Categorical Data.

Degree: Computer Science and Communication (CSC), 2016, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496

►

The Random Forest model is commonly used as a predictor function and the model have been proven useful in a variety of applications. Their… (more)

Subjects/Keywords: machine learning; ml; variable importance; vim; random forests; rf; feature selection; variable selection; exploratory data analysis; eda; Computer Sciences; Datavetenskap (datalogi)

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Hjerpe, A. (2016). Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496

Not specified: Masters Thesis or Doctoral Dissertation

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

Hjerpe, Adam. “Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data.” 2016. Thesis, KTH. Accessed April 12, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hjerpe, Adam. “Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data.” 2016. Web. 12 Apr 2021.

Vancouver:

Hjerpe A. Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data. [Internet] [Thesis]. KTH; 2016. [cited 2021 Apr 12]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hjerpe A. Computing Random Forests Variable Importance Measures (VIM) on Mixed Numerical and Categorical Data. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185496

Not specified: Masters Thesis or Doctoral Dissertation

Kansas State University

17.
Ren, Jie.
High-dimensional *variable* *selection* for genomics data, from both
frequentist and Bayesian perspectives.

Degree: PhD, Department of Statistics, 2020, Kansas State University

URL: http://hdl.handle.net/2097/40513

► *Variable* *selection* is one of the most popular tools for analyzing high-dimensional genomic data. It has been developed to accommodate complex data structures and lead…
(more)

Subjects/Keywords: High‐dimensional data; Network‐based regularization; Robust variable selection; Bayesian variable selection; Gene-environment interactions; Markov chain Monte Carlo

Record Details Similar Records

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

APA (6^{th} Edition):

Ren, J. (2020). High-dimensional variable selection for genomics data, from both frequentist and Bayesian perspectives. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/40513

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

Ren, Jie. “High-dimensional variable selection for genomics data, from both frequentist and Bayesian perspectives.” 2020. Doctoral Dissertation, Kansas State University. Accessed April 12, 2021. http://hdl.handle.net/2097/40513.

MLA Handbook (7^{th} Edition):

Ren, Jie. “High-dimensional variable selection for genomics data, from both frequentist and Bayesian perspectives.” 2020. Web. 12 Apr 2021.

Vancouver:

Ren J. High-dimensional variable selection for genomics data, from both frequentist and Bayesian perspectives. [Internet] [Doctoral dissertation]. Kansas State University; 2020. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2097/40513.

Council of Science Editors:

Ren J. High-dimensional variable selection for genomics data, from both frequentist and Bayesian perspectives. [Doctoral Dissertation]. Kansas State University; 2020. Available from: http://hdl.handle.net/2097/40513

University of California – Berkeley

18. Zhu, Ying. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.

Degree: Business Administration, Ph, 2015, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/9vw1524p

► Econometric models based on observational data are often endogenous due to measurement error, autocorrelated errors, simultaneity and omitted variables, non-random sampling, self-*selection*, etc. Parameter estimates…
(more)

Subjects/Keywords: Statistics; Economics; High-dimensional statistics; Lasso; sample selection; semiparametric estimation; sparsity; variable selection

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APA (6^{th} Edition):

Zhu, Y. (2015). Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/9vw1524p

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Thesis, University of California – Berkeley. Accessed April 12, 2021. http://www.escholarship.org/uc/item/9vw1524p.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhu, Ying. “Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications.” 2015. Web. 12 Apr 2021.

Vancouver:

Zhu Y. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2021 Apr 12]. Available from: http://www.escholarship.org/uc/item/9vw1524p.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhu Y. Endogenous Econometric Models and Multi-Stage Estimation in High-Dimensional Settings: Theory and Applications. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/9vw1524p

Not specified: Masters Thesis or Doctoral Dissertation

Technical University of Lisbon

19. Minhoto, Manuel Joaquim Piteira. Selecção de variáveis em estatística multivariada.

Degree: 2009, Technical University of Lisbon

URL: http://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/1877

►

Doutoramento em Matemática e Estatística - Instituto Superior de Agronomia

The problem of *variable* *selection* consists in identifying a k-subset of a set of original…
(more)

Subjects/Keywords: variable selection; multivariate statistics; combinatorial optimization; Heuristics; Pareto optimal

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APA (6^{th} Edition):

Minhoto, M. J. P. (2009). Selecção de variáveis em estatística multivariada. (Thesis). Technical University of Lisbon. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/1877

Not specified: Masters Thesis or Doctoral Dissertation

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

Minhoto, Manuel Joaquim Piteira. “Selecção de variáveis em estatística multivariada.” 2009. Thesis, Technical University of Lisbon. Accessed April 12, 2021. http://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/1877.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Minhoto, Manuel Joaquim Piteira. “Selecção de variáveis em estatística multivariada.” 2009. Web. 12 Apr 2021.

Vancouver:

Minhoto MJP. Selecção de variáveis em estatística multivariada. [Internet] [Thesis]. Technical University of Lisbon; 2009. [cited 2021 Apr 12]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/1877.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Minhoto MJP. Selecção de variáveis em estatística multivariada. [Thesis]. Technical University of Lisbon; 2009. Available from: http://www.rcaap.pt/detail.jsp?id=oai:www.repository.utl.pt:10400.5/1877

Not specified: Masters Thesis or Doctoral Dissertation

Temple University

20.
Stone, Elizabeth Anne.
Multilevel Model *Selection*: A Regularization Approach Incorporating Heredity Constraints.

Degree: PhD, 2013, Temple University

URL: http://digital.library.temple.edu/u?/p245801coll10,234414

►

Statistics

This dissertation focuses on estimation and *selection* methods for a simple linear model with two levels of variation. This model provides a foundation for…
(more)

Subjects/Keywords: Statistics;

Record Details Similar Records

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

APA (6^{th} Edition):

Stone, E. A. (2013). Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,234414

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

Stone, Elizabeth Anne. “Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints.” 2013. Doctoral Dissertation, Temple University. Accessed April 12, 2021. http://digital.library.temple.edu/u?/p245801coll10,234414.

MLA Handbook (7^{th} Edition):

Stone, Elizabeth Anne. “Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints.” 2013. Web. 12 Apr 2021.

Vancouver:

Stone EA. Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2021 Apr 12]. Available from: http://digital.library.temple.edu/u?/p245801coll10,234414.

Council of Science Editors:

Stone EA. Multilevel Model Selection: A Regularization Approach Incorporating Heredity Constraints. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,234414

Cornell University

21.
Kirtland, Kelly Meredith.
Outlier Detection and Multicollinearity in Sequential *Variable* *Selection*: A Least Angle Regression-Based Approach.

Degree: PhD, Statistics, 2017, Cornell University

URL: http://hdl.handle.net/1813/47809

► As lasso regression has grown exceedingly popular as a tool for coping with *variable* *selection* in high-dimensional data, diagnostic methods have not kept pace. The…
(more)

Subjects/Keywords: Statistics; LARS; lasso; multicollinearity; outlier nomination; sequential; variable selection

Record Details Similar Records

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APA (6^{th} Edition):

Kirtland, K. M. (2017). Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/47809

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

Kirtland, Kelly Meredith. “Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach.” 2017. Doctoral Dissertation, Cornell University. Accessed April 12, 2021. http://hdl.handle.net/1813/47809.

MLA Handbook (7^{th} Edition):

Kirtland, Kelly Meredith. “Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach.” 2017. Web. 12 Apr 2021.

Vancouver:

Kirtland KM. Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1813/47809.

Council of Science Editors:

Kirtland KM. Outlier Detection and Multicollinearity in Sequential Variable Selection: A Least Angle Regression-Based Approach. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/47809

University of Georgia

22. Ssegane, Herbert. In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/27824

► Hydrological predictions at a watershed scale are generally made by extrapolating and upscaling hydrological behavior at point and hillslope scales. However, some dominant hydrological drivers…
(more)

Subjects/Keywords: Causal variable selection; Stepwise regression; Principal component analysis

Record Details Similar Records

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APA (6^{th} Edition):

Ssegane, H. (2014). In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/27824

Not specified: Masters Thesis or Doctoral Dissertation

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

Ssegane, Herbert. “In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds.” 2014. Thesis, University of Georgia. Accessed April 12, 2021. http://hdl.handle.net/10724/27824.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ssegane, Herbert. “In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds.” 2014. Web. 12 Apr 2021.

Vancouver:

Ssegane H. In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10724/27824.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ssegane H. In search of causal watershed variables for watershed classification and daily streamflow prediction in ungauged watersheds. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/27824

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

23. Ouyang, Yunbo. Scalable sparsity structure learning using Bayesian methods.

Degree: PhD, Statistics, 2018, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/101264

► Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on…
(more)

Subjects/Keywords: Bayesian statistics; high-dimensional data analysis; variable selection

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Ouyang, Y. (2018). Scalable sparsity structure learning using Bayesian methods. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101264

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

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 12, 2021. http://hdl.handle.net/2142/101264.

MLA Handbook (7^{th} Edition):

Ouyang, Yunbo. “Scalable sparsity structure learning using Bayesian methods.” 2018. Web. 12 Apr 2021.

Vancouver:

Ouyang Y. Scalable sparsity structure learning using Bayesian methods. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2142/101264.

Council of Science Editors:

Ouyang Y. Scalable sparsity structure learning using Bayesian methods. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101264

Texas A&M University

24.
Song, Qifan.
*Variable**Selection* for Ultra High Dimensional Data.

Degree: PhD, Statistics, 2014, Texas A&M University

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

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

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

Record Details Similar Records

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

APA (6^{th} Edition):

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

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

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Doctoral Dissertation, Texas A&M University. Accessed April 12, 2021. http://hdl.handle.net/1969.1/153224.

MLA Handbook (7^{th} Edition):

Song, Qifan. “Variable Selection for Ultra High Dimensional Data.” 2014. Web. 12 Apr 2021.

Vancouver:

Song Q. Variable Selection for Ultra High Dimensional Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1969.1/153224.

Council of Science Editors:

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

Texas A&M University

25. Goddard, Scott D. Restricted Most Powerful Bayesian Tests.

Degree: PhD, Statistics, 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/155108

► Uniformly most powerful Bayesian tests (UMPBTs) are defined to be Bayesian tests that maximize the probability that the Bayes factor against a fixed null hypothesis…
(more)

Subjects/Keywords: Hypothesis tests; g prior; UMPBT; Bayesian variable selection

Record Details Similar Records

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APA (6^{th} Edition):

Goddard, S. D. (2015). Restricted Most Powerful Bayesian Tests. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155108

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

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Doctoral Dissertation, Texas A&M University. Accessed April 12, 2021. http://hdl.handle.net/1969.1/155108.

MLA Handbook (7^{th} Edition):

Goddard, Scott D. “Restricted Most Powerful Bayesian Tests.” 2015. Web. 12 Apr 2021.

Vancouver:

Goddard SD. Restricted Most Powerful Bayesian Tests. [Internet] [Doctoral dissertation]. Texas A&M University; 2015. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1969.1/155108.

Council of Science Editors:

Goddard SD. Restricted Most Powerful Bayesian Tests. [Doctoral Dissertation]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155108

Penn State University

26. Gosik, Kirk D. STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS.

Degree: 2017, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14022kdg139

► Knowledge about how changes in gene expression are encoded by expression quantitative trait loci (eQTLs) is a key to construct the genotype-phenotype map for complex…
(more)

Subjects/Keywords: variable-selection; gene-expression; eQTL; genetic-architecture; epigenetics

Record Details Similar Records

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

APA (6^{th} Edition):

Gosik, K. D. (2017). STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14022kdg139

Not specified: Masters Thesis or Doctoral Dissertation

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

Gosik, Kirk D. “STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS.” 2017. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/14022kdg139.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Gosik, Kirk D. “STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS.” 2017. Web. 12 Apr 2021.

Vancouver:

Gosik KD. STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS. [Internet] [Thesis]. Penn State University; 2017. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/14022kdg139.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gosik KD. STATISTICAL MODELS FOR HIGH DIMENSIONAL SCREENING OF GENETIC AND EPIGENETIC EFFECTS. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/14022kdg139

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

27.
Zhong, Wei.
feature screening and *variable* *selection* for ultrahigh dimensional data analysis.

Degree: 2012, Penn State University

URL: https://submit-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

Record Details Similar Records

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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. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14917

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/14917.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhong, Wei. “feature screening and variable selection for ultrahigh dimensional data analysis.” 2012. Web. 12 Apr 2021.

Vancouver:

Zhong W. feature screening and variable selection for ultrahigh dimensional data analysis. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/14917.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

28.
Lou, Lejia.
Thresholded partial correlation approach for *variable* *selection* in linear models and partially linear models.

Degree: 2013, Penn State University

URL: https://submit-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

Record Details Similar Records

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Lou, Lejia. “Thresholded partial correlation approach for variable selection in linear models and partially linear models.” 2013. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/19634.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lou, Lejia. “Thresholded partial correlation approach for variable selection in linear models and partially linear models.” 2013. Web. 12 Apr 2021.

Vancouver:

Lou L. Thresholded partial correlation approach for variable selection in linear models and partially linear models. [Internet] [Thesis]. Penn State University; 2013. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/19634.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

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

Degree: 2011, Penn State University

URL: https://submit-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

Record Details Similar Records

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/12143.

Not specified: Masters Thesis or Doctoral Dissertation

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. 12 Apr 2021.

Vancouver:

Li J. THE BAYESIAN LASSO, BAYESIAN SCAD AND BAYESIAN GROUP LASSO WITH APPLICATIONS TO GENOME-WIDE ASSOCIATION STUDIES . [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/12143.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

North Carolina State University

30.
Huang, Lingkang.
*Variable**Selection* in Multiclass Support
Vector Machine and Applications in Genomic Data Analysis.

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

URL: http://www.lib.ncsu.edu/resolver/1840.16/5240

► Microarray techniques provide new insights into cancer diagnosis using gene expression profiles. Molecular diagnosis based on high-throughput genomic data sets presents major challenge due to…
(more)

Subjects/Keywords: multi-class classification; support vector machine; microarray; 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):

Huang, L. (2009). Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5240

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

Huang, Lingkang. “Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis.” 2009. Doctoral Dissertation, North Carolina State University. Accessed April 12, 2021. http://www.lib.ncsu.edu/resolver/1840.16/5240.

MLA Handbook (7^{th} Edition):

Huang, Lingkang. “Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis.” 2009. Web. 12 Apr 2021.

Vancouver:

Huang L. Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2021 Apr 12]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5240.

Council of Science Editors:

Huang L. Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5240