Optimal sampling for repeated binary measurements
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Date
2004
Authors
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Journal ISSN
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Publisher
CANADIAN JOURNAL STATISTICS
Abstract
The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect. To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Muller (2004), which uses a Dirichlet process prior for the mixture.
Description
Keywords
Bayesian decision problem, binary sequence data, Bayesian nonparametric model, optimal sampling, CLINICAL-TRIALS, OUTCOMES