Model-robust quantal regression

dc.contributor.authorNottingham, Quinton J.en
dc.contributor.committeechairBirch, Jeffrey B.en
dc.contributor.committeememberMyers, Raymonden
dc.contributor.committeememberSmith, Eric P.en
dc.contributor.committeememberTerrell, George R.en
dc.contributor.committeememberCoakley, Clint W.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T21:22:31Zen
dc.date.adate2005-10-26en
dc.date.available2014-03-14T21:22:31Zen
dc.date.issued1995en
dc.date.rdate2005-10-26en
dc.date.sdate2005-10-26en
dc.description.abstractIn the analysis of quantal dose-response data, the most commonly used parametric procedure is logistic regression, commonly referred to as "logit analysis." The adequacy of the fit by the logistic regression curve is tested using the chi-square lack-of-fit test. If the lack-of-fit test is not significant, then the logistic model is assumed to be adequate and estimation of effective doses and confidence intervals on the effective doses can be made. When the tolerance distribution of the dose-response data is not known and cannot be assumed by the user, one can use nonparametric methods, such as kernel regression or local linear regression, to estimate the dose-response curve, effective doses, and confidence intervals. This research proposes another alternative to analyzing quantal dose-response data called model-robust quantal regression (MRQR). MRQR linearly combines the parametric and nonparametric predictions with the use of a mixing parameter. MRQR uses logistic regression as the parametric portion of the model and either kernel or local linear regression as the nonparametric portion of the model. Research has shown that the MRQR procedure can improve the fit of the dose-response curve by producing narrower confidence intervals for predictions, while providing improved precision of estimates of the effective doses with respect to logit analysis.en
dc.description.degreePh. D.en
dc.format.extentx, 189 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-10262005-143527en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10262005-143527/en
dc.identifier.urihttp://hdl.handle.net/10919/40225en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1995.N688.pdfen
dc.relation.isformatofOCLC# 34347987en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1995.N688en
dc.titleModel-robust quantal regressionen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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