The Annals of Statistics

Elicitation of Prior Distributions for Variable-Selection Problems in Regression

Paul H. Garthwaite and James M. Dickey

Full-text: Open access


This paper addresses the problem of quantifying expert opinion about a normal linear regression model when there is uncertainty as to which independent variables should be included in the model. Opinion is modeled as a mixture of natural conjugate prior distributions with each distribution in the mixture corresponding to a different subset of the independent variables. It is shown that for certain values of the independent variables, the predictive distribution of the dependent variable simplifies from a mixture of $t$-distributions to a single $t$-distribution. Using this result, a method of eliciting the conjugate distributions of the mixture is developed. The method is illustrated in an example.

Article information

Ann. Statist., Volume 20, Number 4 (1992), 1697-1719.

First available in Project Euclid: 12 April 2007

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Primary: 62F15: Bayesian inference
Secondary: 62C10: Bayesian problems; characterization of Bayes procedures 62J05: Linear regression

Probability assessment methods probability elicitation prior distribution variable selection linear regression


Garthwaite, Paul H.; Dickey, James M. Elicitation of Prior Distributions for Variable-Selection Problems in Regression. Ann. Statist. 20 (1992), no. 4, 1697--1719. doi:10.1214/aos/1176348886.

Export citation