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Bayesian optimal design for changepoint problems

✍ Scribed by Juli Atherton; Benoit Charbonneau; David B. Wolfson; Lawrence Joseph; Xiaojie Zhou; Alain C. Vandal

Publisher: John Wiley and Sons
Year: 2009
Tongue: French
Weight: 191 KB
Volume: 37
Category: Article
ISSN: 0319-5724
DOI: 10.1002/cjs.10037

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

We investigate Bayesian optimal designs for changepoint problems. We find robust optimal designs which allow for arbitrary distributions before and after the change, arbitrary prior densities on the parameters before and after the change, and any log‐concave prior density on the changepoint. We define a new design measure for Bayesian optimal design problems as a means of finding the optimal design. Our results apply to any design criterion function concave in the design measure. We illustrate our results by finding the optimal design in a problem motivated by a previous clinical trial. The Canadian Journal of Statistics 37: 495–513; 2009 © 2009 Statistical Society of Canada

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