Please use this identifier to cite or link to this item: https://doi.org/10.1016/0378-3758(95)00120-4
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dc.titleBayesian design of experiment for quantal responses: What is promised versus what is delivered
dc.contributor.authorSun, D.
dc.contributor.authorTsutakawa, R.K.
dc.contributor.authorLu, W.-S.
dc.contributor.authorTsutakawa, R.K.
dc.date.accessioned2011-02-22T03:09:03Z
dc.date.available2011-02-22T03:09:03Z
dc.date.issued1996
dc.identifier.citationSun, D., Tsutakawa, R.K., Lu, W.-S., Tsutakawa, R.K. (1996). Bayesian design of experiment for quantal responses: What is promised versus what is delivered. Journal of Statistical Planning and Inference 52 (3) : 289-306. ScholarBank@NUS Repository. https://doi.org/10.1016/0378-3758(95)00120-4
dc.identifier.issn03783758
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/19352
dc.description.abstractThis article considers a design problem in quantal response analysis, where an experimenter must choose a set of dose levels and number of independent observations to take at these levels, subject to some total sample size, in order to minimize the expected or predicted posterior variance of some characteristics ? of the tolerance distribution F?, with unknown parameters ?. An exact solution to this problem is demonstrated when ? is the unknown LD50 of the one parameter logistic tolerance distribution, under the restriction that an equal number of observations are taken at each of a set of equally spaced levels. The solution is based on a combination of simulated outcomes and Monte Carlo integration to evaluate the predicted variance. The numerical results are compared to those obtained previously by asymptotic approximations in Tsutakawa (1972), (J. Amer. Statist. Assoc. 67 584-590). The wide variability in the simulated posterior variance suggests that the expected posterior variance alone is not a good criterion for design selection.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/0378-3758(95)00120-4
dc.sourceScopus
dc.subjectLD50
dc.subjectLogistic distribution
dc.subjectMonte Carlo integration
dc.subjectPosterior variance
dc.typeArticle
dc.contributor.departmentECONOMICS & STATISTICS
dc.description.doi10.1016/0378-3758(95)00120-4
dc.description.sourcetitleJournal of Statistical Planning and Inference
dc.description.volume52
dc.description.issue3
dc.description.page289-306
dc.description.codenJSPID
dc.identifier.isiutA1996UP77100003
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