Advanced search options

Sorted by: relevance · author · university · date | New search

You searched for `subject:(SCAD Lasso)`

.
Showing records 1 – 7 of
7 total matches.

▼ Search Limiters

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

URL: https://curate.nd.edu/show/pv63fx73n73

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.theses.fr/2011PA112297

►

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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://etda.libraries.psu.edu/catalog/7084

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://etda.libraries.psu.edu/catalog/9543

► 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…

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

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

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

Record Details Similar Records

❌

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

APA (6^{th} Edition):

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

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

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

MLA Handbook (7^{th} Edition):

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

URL: http://hdl.handle.net/2142/49692

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://www.lib.ncsu.edu/resolver/1840.16/5530

► 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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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