A PREDICTOR-VARIABLE SELECTION PROGRAM

Gene selection: a Bayesian variable selection approach

UNLABELLED Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Baye...

Local Spatial-Predictor Selection

Feature Selection by Conformal Predictor

In this work we consider the problem of feature selection in the context of conformal prediction. Unlike many conventional machine learning methods, conformal prediction allows to supply individual predictions with valid measure of confidence. The main idea is to use confidence measures as an indicator of usefulness of different features: we check how many features are enough to reach desirable...

High Dimensional Variable Selection.

This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we select one model by cross-validation. In the third stage we use hypothesis tes...

Bayesian Shrinkage Variable Selection

We introduce a new Bayesian approach to the variable selection problem which we term Bayesian Shrinkage Variable Selection (BSVS ). This approach is inspired by the Relevance Vector Machine (RVM ), which uses a Bayesian hierarchical linear setup to do variable selection and model estimation. RVM is typically applied in the context of kernel regression although it is also suitable in the standar...