Regressor Levels for Bayesian Predictive Response

Abstract

The predictor vector for a response variable in a full-rank linear model is chosen so as to maximize the probability that the response exceeds a given threshold. The problem is formulated in the Bayesian framework in a way that incorporates collateral information and allows constraints on the levels of the predictors. Industrial applications are indicated.