Variable selection for qualitative interactions
نویسندگان
چکیده
منابع مشابه
Variable Selection for Qualitative Interactions.
In this article we discuss variable selection for decision making with focus on decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is prediction of the response. These techniques often downplay the importance of interaction variables that have small predictive ...
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The Bayesian variable selection method proposed in the paper is based on the evaluation of the Kullback-Leibler distance between the full (or encompassing) model and the submodels. The implementation of the method does not require a separate prior modeling on the submodels since the corresponding parameters for the submodels are deened as the Kullback-Leibler projections of the full model param...
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For many years, subset analysis has been a popular topic for the biostatistics and clinical trials literature. In more recent years, the discussion has focused on finding subsets of genomes which play a role in the effect of treatment, often referred to as stratified or personalized medicine. Though highly sought after, methods for detecting subsets with altering treatment effects are limited a...
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In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated when used in conjunction with the lasso and orthogonal matching pursuit algor...
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ژورنال
عنوان ژورنال: Statistical Methodology
سال: 2011
ISSN: 1572-3127
DOI: 10.1016/j.stamet.2009.05.003