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An empirical Bayes approach to variance function estimation
dc.contributor.advisor | Carroll, Raymond J. | |
dc.creator | Hwang, Lie-Ju | |
dc.date.accessioned | 2020-09-02T20:11:44Z | |
dc.date.available | 2020-09-02T20:11:44Z | |
dc.date.issued | 1990 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/DISSERTATIONS-1163144 | |
dc.description | Typescript (photocopy). | en |
dc.description.abstract | The problem concerns the analysis of assay data when there have been previous similar experiments. Assay data usually fall under the framework of nonlinear regression when the variability about the regression line is non-constant, i.e. heteroscedastic. The typical model for assay data contains parameters β for the mean function and parameters θ for the variance function. Of interest are quantities such as the parameters themselves as well as nonlinear functions of them, e.g. the minimum detectable concentration. There are three basic ways in which such data have been analyzed: (a) take the means of the estimates of β and θ from all assays and use this as the estimate for the current assay; (b) use only the current assay to estimate β and θ and (c) use an empirical Bayes estimate. A small error asymptotic theory, in which all three methods are analyzed in a unified way, has been constructed and the empirical Bayes estimate has a non-normal limit distribution... | en |
dc.format.extent | ix, 120 leaves | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Major statistics | en |
dc.subject.classification | 1990 Dissertation H9915 | |
dc.subject.lcsh | Regression analysis | en |
dc.subject.lcsh | Estimation theory | en |
dc.subject.lcsh | Bayesian statistical decision theory | en |
dc.title | An empirical Bayes approach to variance function estimation | en |
dc.type | Thesis | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.name | Ph. D | en |
dc.contributor.committeeMember | Cline, Daren B. H. | |
dc.contributor.committeeMember | Spielgelman, Clifford H. | |
dc.contributor.committeeMember | Zinn, Joel | |
dc.type.genre | dissertations | en |
dc.type.material | text | en |
dc.format.digitalOrigin | reformatted digital | en |
dc.publisher.digital | Texas A&M University. Libraries | |
dc.identifier.oclc | 23747902 |
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