rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: empirical Bayes approach to variance function estimation
  name:
    name part: Hwang, Lie-Ju
    name part: 1957-
    role:
      role term: creator
  name:
    name part: Carroll, Raymond J.
    role:
      role term: degree supervisor.
  name:
    name part: Cline, Daren B. H.
    role:
      role term: degree committee member.
  name:
    name part: Spielgelman, Clifford H.
    role:
      role term: degree committee member.
  name:
    name part: Zinn, Joel
    role:
      role term: degree committee member.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  genre: Academic theses.
  origin info:
    place:
      place term: xx
    date issued: 1990
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: ix, 120 leaves : illustrations ; 29 cm
  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...
  note: "Major subject: Statistics."
  note: Typescript (photocopy).
  note: Vita.
  note: Ph. D. Texas A & M University 1990
  note: Includes bibliographical references.
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Estimation theory
  subject:
    topic: Regression analysis
  subject:
    topic: Major statistics
  identifier: http://gateway.proquest.com/openurl
  identifier: https://hdl.handle.net/1969.1/DISSERTATIONS-1163144
  identifier: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: http://gateway.proquest.com/openurl
  related item:
    location:
      url: https://hdl.handle.net/1969.1/DISSERTATIONS-1163144
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  record info:
    record content source: TXA
    record creation date: 910515
    record change date: 20220103134838.0
    record identifier: in00001008713
    language of cataloging:
      language term: eng