This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) the response variable. It is shown that posterior feedforward asymptotically consistent under misspecified ALD model. consistency proof embeds problem from density domain and uses bounds on bracketing entropy to derive over Hellinger neighborhoods. result...

When using the K-nearest neighbours (KNN) method, one often ignores the uncertainty in the choice of K. To account for such uncertainty, Bayesian KNN (BKNN) has been proposed and studied (Holmes and Adams 2002 Cucala et al. 2009). We present some evidence to show that the pseudo-likelihood approach for BKNN, even after being corrected by Cucala et al. (2009), still significantly underest...

Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of functional ?(F), where F is random function. Efron’s Rubin’s bootstrap procedures extended, introducing an informative prior through Proper bootstrap. In this paper different techniques are used compared predictive classification regress...