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: Efficient small area estimation in the presence of measurement error in covariates
  name:
    name part: Singh, Trijya.
    role:
      role term: creator
  name:
    name part: Wang, Suojin
    role:
      role term: thesis advisor.
  name:
    name part: Carroll, Raymond J.
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Small area estimation is an arena that has seen rapid development in the past 50 years, due to its widespread applicability in government projects, marketing research and many other areas. However, it is often difficult to obtain error-free data for this purpose. In this dissertation, each project describes a model used for small area estimation in which the covariates are measured with error. We applied different methods of bias correction to improve the estimates of the parameter of interest in the small areas. There is a variety of methods available for bias correction of estimates in the presence of measurement error. We applied the simulation extrapolation (SIMEX), ordinary corrected scores and Monte Carlo corrected scores methods of bias correction in the Fay-Herriot model, and investigated the performance of the bias-corrected estimators. The performance of the estimators in the presence of non-normal measurement error and of the SIMEX estimator in the presence of non-additive measurement error was also studied. For each of these situations, we presented simulation studies to observe the performance of the proposed correction procedures. In addition, we applied our proposed methodology to analyze a real life, nontrivial data set and present the results. We showed that the Lohr-Ybarra estimator is slightly inefficient and that applying methods of bias correction like SIMEX, corrected scores or Monte Carlo corrected scores (MCCS) increases the efficiency of the small area estimates. In particular, we showed that the simulation based bias correction methods like SIMEX and MCCS provide a greater gain in efficiency. We also showed that the SIMEX method of bias correction is robust with respect to departures from normality or additivity of measurement error. We showed that the MCCS method is robust with respect to departure from normality of measurement error.
  note: by Trijya Singh.
  note: "Major Subject: Statistics"
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2011 http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-10010
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Statistics
  subject:
    topic: Corrected Scores
  subject:
    topic: Measurement Error
  subject:
    topic: Small Area Estimation
  subject:
    topic: Covariates
  subject:
    topic: SIMEX
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-10010
  record info:
    record content source: TXA
    record creation date: 121108
    record change date: 20150922140240.0
    record identifier: in00002756503