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: A
    title: quantile function approach to the k-sample quantile regression problem
  name:
    name part: White, James Michael.
    role:
      role term: creator
  name:
    name part: Garcia-Diaz, Alberto
    role:
      role term: degree committee member.
  name:
    name part: Newton, Joseph
    role:
      role term: degree committee member.
  name:
    name part: Parzen, Emanuel
    role:
      role term: degree supervisor.
  name:
    name part: Wehrly, Thomas E.
    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: 1981
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: ix, 113 leaves : illustrations ; 29 cm
  abstract: In this dissertation a procedure for estimating the parameters of a quantile regression function is investigated. The procedure is based on the work of Parzen (1979a) in the theory of quantile functions and is applicable to a wide range of distributional families. The procedure assumes the quantile functions of k populations to be location-scale shifts of a common quantile function. First, a goodness-of-fit procedure for determining the common distributional shape of the k populations generalizes the one-population data modeling techniques of Parzen (1979a). An estimator of the shape parameter of a distribution is also investigated. The methods of Ogawa (1951) and Eubank (1979) are then used for estimating the location and scale parameters of the k populations. A regression model for the location and scale parameters is specified, and the resulting estimators of the regression parameters are used to determine a regression function for any quantiles of the observed data. Finally it is shown that inferences about the quantile relationships can be based on the asymptotic normality of the estimated parameters. The procedures are applied to some published data sets.
  note: "Major subject: Statistics."
  note: Typescript (photocopy).
  note: Vita.
  note: Ph. D. in Philosophy Texas A & M University 1981
  note: Includes bibliographical references (leaves 110-112).
  subject:
    topic: Correlation (Statistics)
  subject:
    topic: Parameter estimation
  subject:
    topic: Regression analysis
  subject:
    topic: Statistics
  identifier: http://proxy.library.tamu.edu/login
  identifier: https://hdl.handle.net/1969.1/DISSERTATIONS-647889
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: https://hdl.handle.net/1969.1/DISSERTATIONS-647889
  record info:
    record content source: TXA
    record creation date: 820115
    record change date: 20220104090529.0
    record identifier: in00000638102
    language of cataloging:
      language term: eng