Bayesian StoNED or Multi-variate Bayesian Convex Regression with inefficiency: Two sides of the same coin.
Bayesian methods allow researchers to simulate regression models to investigate the effects of prior distributional assumptions and modeling restrictions on posterior estimates of model parameters. Here we try to develop a Bayesian version of Stochastic Nonparametric Envelopment of Data (StoNED). There are strong similarities between StoNED and Multi-variate Bayesian Convex Regression (MBCR) proposed by Hannah and Dunson (2013). This talk describes some of these similarities along with some computational results regarding problem size and accuracy of the estimated parameters. We propose an MBCR model with an inefficiency term as a Bayesian StoNED equivalent.