MCMC methods to approximate conditional predictive distributions

Authors:
M. J. Bayarri;M. E. Castellanos;J. Morales
Affiliations:
Department of Statistics and Operations Research, University of Valencia, Valencia 46100, Spain;Department of Statistics and Operations Research, Rey Juan Carlos University, c/Tulipán s/n, Móstoles, Madrid 28933, Spain;Operations Research Center, Miguel Hernández University, Elche 03202, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 1
Cited 1

MCMC algorithms for constrained variance matrices

Computational Statistics & Data Analysis

Obtaining similar null distributions in the normal linear model using computational methods

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.