Gene selection using a two-level hierarchical Bayesian model
Bioinformatics
Bayesian Core: A Practical Approach to Computational Bayesian Statistics (Springer Texts in Statistics)
Evaluating the Stability of Feature Selectors That Optimize Feature Subset Cardinality
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
The Bayesian lasso for genome-wide association studies
Bioinformatics
Computational Statistics & Data Analysis
Data augmentation strategies for the Bayesian spatial probit regression model
Computational Statistics & Data Analysis
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In the Bayesian stochastic search variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner. However there are two standard cases where the associated covariance matrix does not exist and the conventional prior of Zellner cannot be used: if the number of observations is lower than the number of variables (large p and small n paradigm), or if some variables are linear combinations of others. In such situations, a prior distribution derived from the prior of Zellner can be considered by introducing a ridge parameter. This prior is a flexible and simple adaptation of the g-prior and its influence on the selection of variables is studied. A simple way to choose the associated hyper-parameters is proposed. The method is valid for any generalized linear mixed model and particular attention is paid to the study of probit mixed models when some variables are linear combinations of others. The method is applied to both simulated and real datasets obtained from Affymetrix microarray experiments. Results are compared to those obtained with the Bayesian Lasso.