Estimation under purposive sampling

Jacqueline M. Guarte, Erniel B. Barrios

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187 Citations (Scopus)

Abstract

Purposive sampling is described as a random selection of sampling units within the segment of the population with the most information on the characteristic of interest. Nonparametric bootstrap is proposed in estimating location parameters and the corresponding variances. An estimate of bias and a measure of variance of the point estimate are computed using the Monte Carlo method. The bootstrap estimator of the population mean is efficient and consistent in the homogeneous, heterogeneous, and two-segment populations simulated. The design-unbiased approximation of the standard error estimate differs substantially from the bootstrap estimate in severely heterogeneous and positively skewed populations.

Original languageEnglish
Pages (from-to)277-284
Number of pages8
JournalCommunications in Statistics - Simulation and Computation
Volume35
Issue number2
DOIs
Publication statusPublished - Apr 2006
Externally publishedYes

Keywords

  • and two-segment populations
  • Design-unbiased approximation
  • Heterogeneous
  • Homogeneous
  • Nonparametric bootstrap

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