UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

The choice of variables in multivariate regression: A non-conjugate Bayesian decision theory approach

Brown, PJ; Fearn, T; Vannucci, M; (1999) The choice of variables in multivariate regression: A non-conjugate Bayesian decision theory approach. BIOMETRIKA , 86 (3) 635 - 648.

Full text not available from this repository.

Abstract

We consider the choice of explanatory variables in multivariate linear regression. Our approach balances prediction accuracy against costs attached to variables:in a multivariate version of a decision theory approach pioneered by Lindley (1968). We also employ a non-conjugate proper prior distribution for the parameters of the regression model, extending the standard normal-inverse Wishart by adding a component of error which is unexplainable by any number of predictor variables, thus avoiding the determinism identified by Dawid (1988). Simulated annealing and fast updating algorithms are used to search for good subsets when there are very many regressors. The technique is illustrated on a near infrared spectroscopy example involving 39 observations and 300 explanatory variables. This demonstrates the effectiveness of multivariate regression as opposed to separate univariate regressions. It also emphasises that within a Bayesian framework more variables than observations can be utilised.

Type:Article
Title:The choice of variables in multivariate regression: A non-conjugate Bayesian decision theory approach
Keywords:Bayesian decision theory, determinism, multivariate regression, near infrared spectroscopy, non-conjugate distribution, prediction, quadratic loss, simulated annealing, utility, LINEAR-REGRESSION, SELECTION, PREDICTION
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

Archive Staff Only: edit this record