In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian
distributions, we present a joint approach to estimate the number of mixture components and …

We propose a Bayesian variable selection approach for ultrahigh dimensional linear
regression based on the strategy of split and merge. The approach proposed consists of two …

We consider Bayesian model selection in generalized linear models that are high-
dimensional, with the number of covariates p being large relative to the sample size n, but …

Good large sample performance is typically a minimum requirement of any model selection
criterion. This article focuses on the consistency property of the Bayes factor, a commonly …

In all areas of human knowledge, datasets are increasing in both size and complexity,
creating the need for richer statistical models. This trend is also true for economic data …

Bayesian variable selection often assumes normality, but the effects of model
misspecification are not sufficiently understood. There are sound reasons behind this …

Bayesian Effect Selection in Structured Additive Distributional Regression Models Page 1
Bayesian Analysis (2021) 16, Number 2, pp. 545–573 Bayesian Effect Selection in Structured …

Short-term forecasting is an important tool in understanding environmental processes. In this
paper, we incorporate machine learning algorithms into a conditional distribution estimator …

Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups
considered are usually specialized and often involves independent and identically …

There has been an intense development in the Bayesian graphical model literature over the
past decade; however, most of the existing methods are restricted to moderate dimensions …