title: Predictive variable selection for the multivariate linear model.
abstract: We develop a predictive Bayesian approach to variable selection in the multivariate linear model. A criterion derived from the Bayesian predictive density is proposed and a calibration is provided for it. Reference and informative priors are discussed, and an automated method that focuses on the response variable is proposed for specifying informative priors for the regression parameters. Relationships between the proposed criterion and other several well-known criteria are examined. Illustrative examples involving real data are given to demonstrate the methodology.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
authors.forename: M H
authors.initials: MH
authors.lastname: Chen
mesh_terms: D001194:Asbestos / Q000009:adverse effects; D001499:Bayes Theorem; D001699:Biometry; D005541:Forced Expiratory Volume; D006801:Humans; D016014:Linear Models; D008168:Lung / Q000187:drug effects / Q000503:physiopathology; D016273:Occupational Exposure; D014797:Vital Capacity
chemical_list: D001194:Asbestos