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A nonparametric mean estimator for judgment poststratified data.

Xinlei Wang1, Johan Lim, Lynne Stokes

  • 1Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P.O. Box 750332, Dallas, Texas 75275-0332, USA. swang@mail.smu.edu

Biometrics
|March 8, 2008
PubMed
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This study improves judgment poststratification (JPS) mean estimation by using ordered data. The new method is more efficient, especially for small sample sizes, and handles imprecise rankings.

Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • Judgment poststratification (JPS) is a data collection method.
  • Existing JPS methods for mean estimation have limitations.

Purpose of the Study:

  • To propose an improved mean estimator for JPS samples.
  • To enhance efficiency and handle complex ranking scenarios in JPS.

Main Methods:

  • Developed a new mean estimator using isotonized sample means of poststrata.
  • Exploited stochastic ordering of judgment poststrata distributions.
  • Extended methods for imprecise ranking and multiple rankers.

Main Results:

  • The proposed estimator is strongly consistent with similar asymptotic properties to prior JPS methods.

Related Experiment Videos

  • Demonstrated improved efficiency for small sample sizes.
  • Evaluated performance on three data examples via simulation.

    • Conclusions:

    • The enhanced JPS method offers greater efficiency, particularly for cost-sensitive applications.
    • The approach is robust to imprecise ranking and multiple rankers in JPS samples.