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:
    title: Structural logistic regression measurement error models
  name:
    name part: Stefanski, Leonard A.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Carroll, Raymond J.
    role:
      role term: author.
  name:
    name part: Texas A & M University
    name part: Department of Statistics,
    role:
      role term: issuing body.
  name:
    name part: National Institutes of Health (U.S.)
    role:
      role term: sponsoring body.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 1989
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: 9 leaves, 3 unnumbered leaves ; 28 cm
  abstract: We study structural measurement error models when the reponse Y and true predictor X follow a logistic regression model and the observed proxy W satisfies a conditional independence assumption. Assuming that a structural measurement error model is appropriate, we compare two approaches to estimation. In the first approach no assumptions about the distribution of X are made; in the second approach it is assumed that X has a normal distribution. When the normality assumption holds, the second approach yields a consistent and asymptotically efficient estimator; however, consistency is lost when the normality assumption is violated. The first approach yields consistent estimators irrespective of the distribution of X; however, it sacrifices efficiency when X is normally distributed. In the latter case we examine the loss of efficiency for some parametric specifications of known importance.
  note: L.A. Stefanski and R.J. Carroll.
  note: Funding information taken from leaf 8.
  note: Offprint: Proceedings of the Arcata Conference on Errors-in-variable.
  note: Includes bibliographical references (leaves 8-9).
  note: Supported by the National Institutes of Health
  subject:
    topic: Errors-in-variables models
  subject:
    topic: Logistic regression analysis
  subject:
    topic: Error analysis (Mathematics)
  subject:
    topic: Estimation theory
  subject:
    topic: Regression analysis
  subject:
    topic: Mathematical statistics
  subject:
    topic: Statistics
    topic: Nonparametric inference
    topic: Nonparametric regression
  subject:
    topic: Statistics
    topic: Parametric inference
    topic: Asymptotic properties of estimators
  subject:
    topic: Statistics
    topic: Nonparametric inference
    topic: Asymptotic properties
  subject:
    topic: Asymptotic relative efficiency
  subject:
    topic: Measurement error models
  subject:
    topic: Semiparametric regression
  subject:
    topic: Conditional score
  subject:
    topic: Surrogate variables
  classification: QA276.A12 T4 no.73
  related item:
    title info:
      title: Structural logistic regression measurement error models
    name:
      name part: Stefanski, Leonard A.
    origin info:
      publisher: College Station, Texas : Department of Statistics, Texas A & M University, [1990]
    identifier: (OCoLC)1030893971
  related item:
    title info:
      title: Technical report (Texas A & M University. Department of Statistics) ; no. 73
  record info:
    record content source: TXA
    record creation date: 180409
    record change date: 20180409095935.0
    record identifier: in00003954042
    language of cataloging:
      language term: eng