Bayesian look ahead one-stage sampling allocations for selection of the best population

From k independent normal populations with unknown means and a common known variance, Bayesian selection procedures are considered for finding that population which has the largest mean. Suppose that a first stage has been completed already, where k samples have been observed, which may be of differ...

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Bibliographic Details
Published in:Journal of statistical planning and inference 1996-09, Vol.54 (2), p.229-244
Main Authors: Gupta, Shanti S., Miescke, Klaus J.
Format: Article
Language:English
Subjects:
Bayesian look ahead
Normal populations
Optimum sampling allocations
Selection and ranking
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Summary:From k independent normal populations with unknown means and a common known variance, Bayesian selection procedures are considered for finding that population which has the largest mean. Suppose that a first stage has been completed already, where k samples have been observed, which may be of different sizes. Let there be m additional observations allowed to be taken at a future second stage. The problem of interest treated here is how to allocate these m observations in an optimum way among the k populations, given all of the information, prior and first stage observations, gathered so far. This allocation problem will be formulated and discussed in a more general framework, and specific results will be presented for the normal case with independent conjugate priors under linear loss.
ISSN:0378-3758
1873-1171
DOI:10.1016/0378-3758(95)00169-7