This article is concerned with the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals which implement, respectively, the exact Bayesian Model Averaging (BMA) estimator and the Weighted Average Least...

We consider an approximate posterior approach to making joint probabilistic inference on the action and the associated risk in data mining. The posterior probability is based on a profile empirical likelihood, which imposes a moment restriction relating the action to the resulting risk, but does...

A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covariance matrix that is a mixture over all decomposable graphs. For this prior the probability of each graph size is...

The naive Bayes approach is one of the most popular methods used for classification. Nevertheless, how to test its statistical significance under an ultra-high-dimensional (UHD) setup is not well understood. To fill this important theoretical gap, we propose a novel testing statistic with a...

The paper proposes a new Hierarchical Bayes method to derive high-frequency series consistent with less-frequency data, using indicators. The method allows for the prior setting of covariates between the high-frequency values and it rewards with a greater prior probability the smoothness of the...