UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Multivariate Bayesian variable selection and prediction

Brown, PJ; Vannucci, M; Fearn, T; (1998) Multivariate Bayesian variable selection and prediction. J ROY STAT SOC B , 60 627 - 641.

Full text not available from this repository.


The multivariate regression model is considered with p regressors. A latent vector with p binary entries serves to identify one of two types of regression coefficients: those close to 0 and those not. Specializing our general distributional setting to the linear model with Gaussian errors and using natural conjugate prior distributions, we derive the marginal posterior distribution of the binary latent vector. Fast algorithms aid its direct computation, and in high dimensions these are supplemented by a Markov chain Monte Carlo approach to sampling from the known posterior distribution. Problems with hundreds of regressor variables become quite feasible. We give a simple method of assigning the hyperparameters of the prior distribution. The posterior predictive distribution is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infra-red absorbances as regressors.

Type: Article
Title: Multivariate Bayesian variable selection and prediction
Keywords: Bayesian selection, conjugate distributions, latent variables, Markov chain Monte Carlo method, model averaging, multivariate regression, prediction, MODEL SELECTION, REGRESSION
URI: http://discovery.ucl.ac.uk/id/eprint/151985
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item