dc.creator | Quintana, FA | |
dc.creator | Muller, P | |
dc.date.accessioned | 2024-01-10T13:51:35Z | |
dc.date.available | 2024-01-10T13:51:35Z | |
dc.date.created | 2024-01-10T13:51:35Z | |
dc.date.issued | 2004 | |
dc.identifier | 10.2307/3316000 | |
dc.identifier | 0319-5724 | |
dc.identifier | https://doi.org/10.2307/3316000 | |
dc.identifier | https://repositorio.uc.cl/handle/11534/79612 | |
dc.identifier | WOS:000220988500007 | |
dc.description.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. | |
dc.language | en | |
dc.publisher | CANADIAN JOURNAL STATISTICS | |
dc.rights | acceso restringido | |
dc.subject | Bayesian decision problem | |
dc.subject | binary sequence data | |
dc.subject | Bayesian nonparametric model | |
dc.subject | optimal sampling | |
dc.subject | CLINICAL-TRIALS | |
dc.subject | OUTCOMES | |
dc.title | Optimal sampling for repeated binary measurements | |
dc.type | artículo | |