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Article: Bayesian optimal designs for probit regression with errors-in-variables
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TitleBayesian optimal designs for probit regression with errors-in-variables
 
AuthorsTang, PK
Bacon-Shone, J
 
KeywordsOptimal design
Posterior mode
Errors-in-variahles
Berkson's model
Prohit
 
Issue Date1992
 
PublisherUniversity of Hong Kong. Dept. of Statistics.
 
CitationResearch Report, n. 31, p. 1-18 [How to Cite?]
 
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.
 
DC FieldValue
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
 
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.
 
dc.description.naturepostprint
 
dc.identifier.citationResearch Report, n. 31, p. 1-18 [How to Cite?]
 
dc.identifier.urihttp://hdl.handle.net/10722/60979
 
dc.language.isoeng
 
dc.publisherUniversity of Hong Kong. Dept. of Statistics.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.rightsAuthor holds the copyright
 
dc.subjectOptimal design
 
dc.subjectPosterior mode
 
dc.subjectErrors-in-variahles
 
dc.subjectBerkson's model
 
dc.subjectProhit
 
dc.titleBayesian optimal designs for probit regression with errors-in-variables
 
dc.typeArticle
 
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<contributor.author>Bacon-Shone, J</contributor.author>
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<date.available>2010-06-02T04:03:08Z</date.available>
<date.issued>1992-12</date.issued>
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<description.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.</description.abstract>
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<subject>Optimal design</subject>
<subject>Posterior mode</subject>
<subject>Errors-in-variahles</subject>
<subject>Berkson&apos;s model</subject>
<subject>Prohit</subject>
<title>Bayesian optimal designs for probit regression with errors-in-variables</title>
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