dc.creatorQuintana, FA
dc.creatorMuller, P
dc.date.accessioned2024-01-10T13:51:35Z
dc.date.available2024-01-10T13:51:35Z
dc.date.created2024-01-10T13:51:35Z
dc.date.issued2004
dc.identifier10.2307/3316000
dc.identifier0319-5724
dc.identifierhttps://doi.org/10.2307/3316000
dc.identifierhttps://repositorio.uc.cl/handle/11534/79612
dc.identifierWOS:000220988500007
dc.description.abstractThe 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.
dc.languageen
dc.publisherCANADIAN JOURNAL STATISTICS
dc.rightsacceso restringido
dc.subjectBayesian decision problem
dc.subjectbinary sequence data
dc.subjectBayesian nonparametric model
dc.subjectoptimal sampling
dc.subjectCLINICAL-TRIALS
dc.subjectOUTCOMES
dc.titleOptimal sampling for repeated binary measurements
dc.typeartículo


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