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You searched for subject:(Variable selection). Showing records 1 – 30 of 415 total matches.

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University of Manchester

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

Degree: 2018, University of Manchester

 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

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APA (6th 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 (16th 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 (7th 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

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

MLA Handbook (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 April 12, 2021. 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. 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

Degree: 2013, Erasmus University Medical Center

 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

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APA (6th 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 (16th 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 (7th 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

  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

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APA (6th 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 (16th 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 (7th 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

Subjects/Keywords: LASSO; variable selection

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APA (6th Edition):

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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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 April 12, 2021. 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. 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

 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

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APA (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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 April 12, 2021. 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. 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

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)

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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;

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/19634

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12143

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):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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 (16th 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 (7th 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

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