This paper presents the R-package <B>MitISEM</B> (mixture of <I>t</I> by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture...</i></b>

This paper addresses the estimation of the nonparametric conditional moment restricted model that involves an infinite-dimensional parameter g0. We estimate it in a quasi-Bayesian way, based on the limited information likelihood, and investigate the impact of three types of priors on the...

This short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus...

This paper presents the R-package <B>MitISEM</B> (mixture of <I>t</I> by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture...</i></b>

We propose a new nonlinear classification method based on a Bayesian "sum-of-trees" model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive...

This paper presents the R package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel - typically a posterior density kernel - using an adaptive mixture of...

This paper introduces a Bayesian MCMC method, referred to as a marginalized mixture sampler, for state space models whose disturbances follow stochastic volatility processes. The marginalized mixture sampler is based on a mixture-normal approximation of the log-2 distribution, but it is...

This paper presents a Bayesian significance test for stationarity of a regression equation using the highest posterior density credible set. In addition, a solution to the Behrens- Fisher problem is provided. From a Monte Carlo simulation study, it has been shown that the Bayesian significance...

Survival data often include an “immune” or cured fraction of units that will never experience an event and conversely, an “at risk” fraction that can fail or die. It is also plausible that spatial clustering (i.e., spatial autocorrelation) in latent or unmeasured risk factors among...

Understanding individual customers’ sensitivities to prices, promotions, brand, and other aspects of the marketing mix is fundamental to a wide swath of marketing problems, including targeting and pricing. Companies that operate across many product categories have a unique opportunity, insofar...