The Annals of Statistics
- Ann. Statist.
- Volume 19, Number 3 (1991), 1244-1256.
Robust Bayesian Experimental Designs in Normal Linear Models
We address the problem of finding a design that minimizes the Bayes risk with respect to a fixed prior subject to being robust with respect to misspecification of the prior. Uncertainty in the prior is formulated in terms of having a family of priors instead of one single prior. Two different classes of priors are considered: $\Gamma_1$ is a family of conjugate priors, and a second family of priors $\Gamma_2$ is induced by a metric on the space of nonnegative measures. The family $\Gamma_1$ has earlier been suggested by Leamer and Polasek, while $\Gamma_2$ was considered by DeRobertis and Hartigan and Berger. The setup assumed is that of a canonical normal linear model with independent homoscedastic errors. Optimal robust designs are considered for the problem of estimating the vector of regression coefficients or a linear combination of the regression coefficients and also for testing and set estimation problems. Concrete examples are given for polynomial regression and completely randomized designs. A very surprising finding is that for $\Gamma_2$, the same design is optimal for a variety of different problems with different loss structures. In general, the results for $\Gamma_2$ are significantly more substantive. Our results are applicable to group decision making and reconciliation of opinions among experts with different priors.
Ann. Statist., Volume 19, Number 3 (1991), 1244-1256.
First available in Project Euclid: 12 April 2007
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Robust Bayesian design
DasGupta, A.; Studden, W. J. Robust Bayesian Experimental Designs in Normal Linear Models. Ann. Statist. 19 (1991), no. 3, 1244--1256. doi:10.1214/aos/1176348247. https://projecteuclid.org/euclid.aos/1176348247