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An Efficient Stochastic Search for Bayesian Variable Selection with High-Dimensional Correlated Predictors.
MedLine Citation:
PMID:  21686315     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We present a Bayesian variable selection method for the setting in which the number of independent variables or predictors in a particular dataset is much larger than the available sample size. While most existing methods allow some degree of correlations among predictors but do not consider these correlations for variable selection, our method accounts for correlations among the predictors in variable selection. Our correlation-based stochastic search (CBS) method, the hybrid-CBS algorithm, extends a popular search algorithm for high-dimensional data, the stochastic search variable selection (SSVS) method. Similar to SSVS, we search the space of all possible models using variable addition, deletion or swap moves. However, our moves through the model space are designed to accommodate correlations among the variables. We describe our approach for continuous, binary, ordinal, and count outcome data. The impact of choices of prior distributions and hyper-parameters is assessed in simulation studies. We also examined performance of variable selection and prediction as the correlation structure of the predictors varies. We found that the hybrid-CBS resulted in lower prediction errors and better identified the true outcome associated predictors than SSVS when predictors were moderately to highly correlated. We illustrate the method on data from a proteomic profiling study of melanoma, a skin cancer.
Authors:
Deukwoo Kwon; Maria Teresa Landi; Marina Vannucci; Haleem J Issaq; Darue Prieto; Ruth M Pfeiffer
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Computational statistics & data analysis     Volume:  55     ISSN:  0167-9473     ISO Abbreviation:  -     Publication Date:  2011 Oct 
Date Detail:
Created Date:  2011-6-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100960938     Medline TA:  Comput Stat Data Anal     Country:  -    
Other Details:
Languages:  ENG     Pagination:  2807-2818     Citation Subset:  -    
Affiliation:
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland 20852, U.S.A.
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
NIH0012345641//PHS HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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