Bayesian bootstrap multivariate regression

摘要

A Bayesian bootstrap multivariate regression (BBMR) procedure is presented that allows robust Bayesian analysis of multivariate regression models. BBMR does not require a parametric specification for the likelihood function and instead uses a bootstrapped likelihood based on the sampling distribution of location and scale estimators. A mixing algorithm for implementing the procedure automatically incorporates the scale invariant ignorance prior on the covariance matrix. BBMR can be implemented as ageneric algorithm in standard statistical software independently of the actual choice of prior distribution. Monte Carlo evidence is provided showing accuracy and robustness of the approach in representing posterior distributions.