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September, 1973 Asymptotically Optimal Bayes Sequential Design of Experiments for Estimation
Victor J. Yohai
Ann. Statist. 1(5): 822-837 (September, 1973). DOI: 10.1214/aos/1176342504


The purpose of this paper is to find asymptotically optimal Bayes sequential procedures for estimating a function $g(\theta_1, \theta_2,\cdots, \theta_k)$ when there are $k$ experiments $E_1, E_2,\cdots, E_k$ and the performance of the experiment $E_i$ conducts to the observation of a random variable whose distribution depends on the vector parameter $\theta_i$. The term asymptotical refers here to the cost of experimentation tending to zero. The methods used are a generalization of those introduced by Bickel and Yahav.


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Victor J. Yohai. "Asymptotically Optimal Bayes Sequential Design of Experiments for Estimation." Ann. Statist. 1 (5) 822 - 837, September, 1973.


Published: September, 1973
First available in Project Euclid: 12 April 2007

zbMATH: 0288.62036
MathSciNet: MR339416
Digital Object Identifier: 10.1214/aos/1176342504

Keywords: 45 , 62 , asymptotical optimality , Bayesian estimation , sequential design

Rights: Copyright © 1973 Institute of Mathematical Statistics


Vol.1 • No. 5 • September, 1973
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