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Article: Bayesian optimal designs for probit regression with errors-in-variables

TitleBayesian optimal designs for probit regression with errors-in-variables
Authors
KeywordsOptimal design
Posterior mode
Errors-in-variahles
Berkson's model
Prohit
Issue DateDec-1992
PublisherUniversity of Hong Kong. Dept. of Statistics.
Citation
Research Report, n. 31, p. 1-18 How to Cite?
Abstract
Optimal design is the study of the choice of design points in an experiment. However, measurements are seldom precise in practical situations. If measurement error is substantial, it may ruin the whole experiment in that the objective of the experiment is not achieved. There is substantial literature on optimal designs, all based on the assumption that there is no measurement error in the covariates. For the Berkson error model, the observed design points are fixed by the experimenter but they deviate randomly from the pre-assigned level. In this paper, the Berkson error structure is incorporated into the probit regression model for which Bayesian D-optimal and A-optimal designs are studied. In addition, a new optimal design criterion is proposed.
Persistent Identifierhttp://hdl.handle.net/10722/60979

 

DC FieldValueLanguage
dc.contributor.authorTang, PK-
dc.contributor.authorBacon-Shone, J-
dc.date.accessioned2010-06-02T04:03:08Z-
dc.date.available2010-06-02T04:03:08Z-
dc.date.issued1992-12-
dc.identifier.citationResearch Report, n. 31, p. 1-18en_HK
dc.identifier.urihttp://hdl.handle.net/10722/60979-
dc.description.abstractOptimal design is the study of the choice of design points in an experiment. However, measurements are seldom precise in practical situations. If measurement error is substantial, it may ruin the whole experiment in that the objective of the experiment is not achieved. There is substantial literature on optimal designs, all based on the assumption that there is no measurement error in the covariates. For the Berkson error model, the observed design points are fixed by the experimenter but they deviate randomly from the pre-assigned level. In this paper, the Berkson error structure is incorporated into the probit regression model for which Bayesian D-optimal and A-optimal designs are studied. In addition, a new optimal design criterion is proposed.en_HK
dc.language.isoengen_HK
dc.publisherUniversity of Hong Kong. Dept. of Statistics.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsAuthor holds the copyright-
dc.subjectOptimal designen_HK
dc.subjectPosterior modeen_HK
dc.subjectErrors-in-variahlesen_HK
dc.subjectBerkson's modelen_HK
dc.subjectProhiten_HK
dc.titleBayesian optimal designs for probit regression with errors-in-variablesen_HK
dc.typeArticleen_HK
dc.description.naturepostprint-

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