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

• 成果类型: Article
• 署名作者: Brown, PJ; Fearn, T; Vannucci, M
• 署名单位: University of Kent; University of London; University College London; Texas A&M University System; Texas A&M University College Station
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• 发表日期: 1999
• 页码: 635648
• 关键词: LINEAR-REGRESSION; selection; prediction

摘要

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.