Please use this identifier to cite or link to this item: https://doi.org/10.1155/2012/642403
Title: A two-stage penalized logistic regression approach to case-control genome-wide association studies
Authors: Zhao, J.
Chen, Z. 
Issue Date: 2012
Citation: Zhao, J., Chen, Z. (2012). A two-stage penalized logistic regression approach to case-control genome-wide association studies. Journal of Probability and Statistics : -. ScholarBank@NUS Repository. https://doi.org/10.1155/2012/642403
Abstract: We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by using L1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and Jeffreys Prior penalty (Firth, 1993), a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008). The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS) project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005) and the LASSO-patternsearch algorithm (Shi et al. 2007). © 2012 Jingyuan Zhao and Zehua Chen.
Source Title: Journal of Probability and Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/104982
ISSN: 1687952X
DOI: 10.1155/2012/642403
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