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You searched for subject:(SCAD Lasso). Showing records 1 – 7 of 7 total matches.

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University of Notre Dame

1. Weiye Chen. Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>.

Degree: PhD, Applied and Computational Mathematics and Statistics, 2015, University of Notre Dame

  My work in this thesis is about the lasso-based penalties, SCAD-based penalties and their applications to genetic research and economic forecasting. Motivated by functional… (more)

Subjects/Keywords: macroeconomic forecasting; SCAD; Lasso; fGWAS

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APA (6th Edition):

Chen, W. (2015). Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/pv63fx73n73

Chicago Manual of Style (16th Edition):

Chen, Weiye. “Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>.” 2015. Doctoral Dissertation, University of Notre Dame. Accessed August 24, 2019. https://curate.nd.edu/show/pv63fx73n73.

MLA Handbook (7th Edition):

Chen, Weiye. “Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>.” 2015. Web. 24 Aug 2019.

Vancouver:

Chen W. Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2015. [cited 2019 Aug 24]. Available from: https://curate.nd.edu/show/pv63fx73n73.

Council of Science Editors:

Chen W. Penalized Methods and Their Applications to Genetic Research and Economic Forecasting</h1>. [Doctoral Dissertation]. University of Notre Dame; 2015. Available from: https://curate.nd.edu/show/pv63fx73n73


Université Paris-Sud – Paris XI

2. El anbari, Mohammed. Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée.

Degree: Docteur es, Mathématiques, 2011, Université Paris-Sud – Paris XI

Dans cette thèse nous nous intéressons aux problèmes de la sélection de variables en régression linéaire. Ces travaux sont en particulier motivés par les développements… (more)

Subjects/Keywords: Réduction de la dimension; Grandes dimensions; Lasso; Scad; Elastic-net; Sélection de modèles; Propriétés d’Oracle; Zellner’s g- prior; Calibration; Dimensionality réduction; High dimensionality; LASSO; Scad; Elastic-net; Model selection; Oracle property; Zellner’s g-prior; Calibration; Scad

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APA (6th Edition):

El anbari, M. (2011). Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2011PA112297

Chicago Manual of Style (16th Edition):

El anbari, Mohammed. “Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée.” 2011. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed August 24, 2019. http://www.theses.fr/2011PA112297.

MLA Handbook (7th Edition):

El anbari, Mohammed. “Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée.” 2011. Web. 24 Aug 2019.

Vancouver:

El anbari M. Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2011. [cited 2019 Aug 24]. Available from: http://www.theses.fr/2011PA112297.

Council of Science Editors:

El anbari M. Regularisation and variable selection using penalized likelihood : Régularisation et sélection de variables par le biais de la vraisemblance pénalisée. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2011. Available from: http://www.theses.fr/2011PA112297


Penn State University

3. Dziak, John Joseph. PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH.

Degree: PhD, Statistics, 2006, Penn State University

 For decades, much research has been devoted to developing and comparing variable selection methods, but primarily for the classical case of independent observations. Existing variable-selection… (more)

Subjects/Keywords: SCAD; LASSO; QIF; GEE; generalized estimating equations; variable selection

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APA (6th Edition):

Dziak, J. J. (2006). PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/7084

Chicago Manual of Style (16th Edition):

Dziak, John Joseph. “PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH.” 2006. Doctoral Dissertation, Penn State University. Accessed August 24, 2019. https://etda.libraries.psu.edu/catalog/7084.

MLA Handbook (7th Edition):

Dziak, John Joseph. “PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH.” 2006. Web. 24 Aug 2019.

Vancouver:

Dziak JJ. PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH. [Internet] [Doctoral dissertation]. Penn State University; 2006. [cited 2019 Aug 24]. Available from: https://etda.libraries.psu.edu/catalog/7084.

Council of Science Editors:

Dziak JJ. PENALIZED QUADRATIC INFERENCE FUNCTIONS FOR VARIABLE SELECTION IN LONGITUDINAL RESEARCH. [Doctoral Dissertation]. Penn State University; 2006. Available from: https://etda.libraries.psu.edu/catalog/7084

4. Zhang, Yiyun. Regularization Parameter Selection for Variable Selection in High-dimensional Modelling.

Degree: PhD, Statistics, 2009, Penn State University

 Variable selection is an important issue in statistical modelling. Classical approaches select models by applying a penalty related to the size of the candidate model.… (more)

Subjects/Keywords: GLIM; LASSO; Penalized Likelihood; SCAD; Variable Selection

…1993), the LASSO (Tibshirani, 1996) and the SCAD (Fan & Li, 2001)… …x28;2007b) showed that SCAD penalized least squares estimate with BIC tuning parameter… …candidate model set. However, their results are limited to linear regression with SCAD penalty. In… …Penalties In the past decade, many continuous penalties such as the LASSO or L1 penalty (… …Tibshirani, 1996), and the SCAD penalty (Fan & Li, 2001) have been proposed for… 

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APA (6th Edition):

Zhang, Y. (2009). Regularization Parameter Selection for Variable Selection in High-dimensional Modelling. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9543

Chicago Manual of Style (16th Edition):

Zhang, Yiyun. “Regularization Parameter Selection for Variable Selection in High-dimensional Modelling.” 2009. Doctoral Dissertation, Penn State University. Accessed August 24, 2019. https://etda.libraries.psu.edu/catalog/9543.

MLA Handbook (7th Edition):

Zhang, Yiyun. “Regularization Parameter Selection for Variable Selection in High-dimensional Modelling.” 2009. Web. 24 Aug 2019.

Vancouver:

Zhang Y. Regularization Parameter Selection for Variable Selection in High-dimensional Modelling. [Internet] [Doctoral dissertation]. Penn State University; 2009. [cited 2019 Aug 24]. Available from: https://etda.libraries.psu.edu/catalog/9543.

Council of Science Editors:

Zhang Y. Regularization Parameter Selection for Variable Selection in High-dimensional Modelling. [Doctoral Dissertation]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9543


Bowling Green State University

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Doctoral Dissertation, Bowling Green State University. Accessed August 24, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

MLA Handbook (7th Edition):

Yousef, Mohammed A. “Two-Stage SCAD Lasso for Linear Mixed Model Selection.” 2019. Web. 24 Aug 2019.

Vancouver:

Yousef MA. Two-Stage SCAD Lasso for Linear Mixed Model Selection. [Internet] [Doctoral dissertation]. Bowling Green State University; 2019. [cited 2019 Aug 24]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.

Council of Science Editors:

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


University of Illinois – Urbana-Champaign

6. Gan, Lu. Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement.

Degree: PhD, 0329, 2014, University of Illinois – Urbana-Champaign

 Many variable selection methods are available for linear regression but very little has been developed for quantile regression, especially for the censored problems. This study… (more)

Subjects/Keywords: Variable Screening; Censored Data; Quantile Regression; Least Absolute Selection and Shrinkage Operator (LASSO); Smoothly Clipped Absolute Deviation (SCAD); Portnoy; Peng and Huang; Stepwise Regression; Bidirectional; Backward; Left Censoring; Random Censoring

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

APA (6th Edition):

Gan, L. (2014). Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/49692

Chicago Manual of Style (16th Edition):

Gan, Lu. “Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 24, 2019. http://hdl.handle.net/2142/49692.

MLA Handbook (7th Edition):

Gan, Lu. “Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement.” 2014. Web. 24 Aug 2019.

Vancouver:

Gan L. Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/2142/49692.

Council of Science Editors:

Gan L. Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/49692


North Carolina State University

7. Chen, Yun. False Selection Rate Methods in the Cox Proportional Hazards Model.

Degree: PhD, Statistics, 2006, North Carolina State University

 Variable selection methods are useful for distinguishing informative variables from uninformative variables. Many variable selection methods have been studied in linear regression models. Some methods… (more)

Subjects/Keywords: SCAD-FSR; model selection; Forward-FSR; false selection rate (FSR); LASSO-FSR; the Cox model

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

APA (6th Edition):

Chen, Y. (2006). False Selection Rate Methods in the Cox Proportional Hazards Model. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5530

Chicago Manual of Style (16th Edition):

Chen, Yun. “False Selection Rate Methods in the Cox Proportional Hazards Model.” 2006. Doctoral Dissertation, North Carolina State University. Accessed August 24, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5530.

MLA Handbook (7th Edition):

Chen, Yun. “False Selection Rate Methods in the Cox Proportional Hazards Model.” 2006. Web. 24 Aug 2019.

Vancouver:

Chen Y. False Selection Rate Methods in the Cox Proportional Hazards Model. [Internet] [Doctoral dissertation]. North Carolina State University; 2006. [cited 2019 Aug 24]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5530.

Council of Science Editors:

Chen Y. False Selection Rate Methods in the Cox Proportional Hazards Model. [Doctoral Dissertation]. North Carolina State University; 2006. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5530

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