title: Bayesian optimal designs for estimating a set of symmetrical quantiles.
abstract: We propose multiple-objective Bayesian optimal designs for the logit model. As an example, we consider the design problem for estimating several percentiles with possibly unequal interest in each of the percentiles. Characteristics of these designs are studied and illustrated for the case when the interest lies in estimating the three quartiles. We compare these optimal designs with the sequential designs generated via a generalized Pólya urn model and found the latter to be highly efficient. In addition, comparisons are made between locally optimal designs and Bayesian optimal designs.
authors.affiliation: Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, NY 11794-3600, USA. zhu@ams.sunysb.edu
authors.forename: W
authors.initials: W
authors.lastname: Zhu
authors.lastname: Kee Wong W
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D011597:Psychomotor Performance; D012107:Research Design; D012684:Sensory Thresholds