Progressive random sampling with stratification

Plinio A. De los Santos, Richard J. Burke, James M. Tien

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A number of applications, including claims made under federal social welfare programs, requires retrospective sampling over multiple time periods. A common characteristic of such samples is that population members could appear in multiple time periods. When this occurs, and when the marginal cost of obtaining multiperiod information is minimum for a member appearing in the sample of the period being actively sampled, the progressive random sampling (PRS) method developed by the authors earlier can be applied. This paper enhances the progressive random sampling method by combining it with stratification schemes; the resultant stratified progressive random sampling (SPRS) technique is shown to provide significant improvement over traditional sampling techniques whenever stratification is appropriate. An empirical example based on a data transformation of a real-world application is provided to illustrate the practical application of the technique.

Original languageEnglish (US)
Pages (from-to)1223-1230
Number of pages8
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume37
Issue number6
DOIs
StatePublished - Nov 1 2007
Externally publishedYes

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Keywords

  • Employee welfare
  • False Claims Act
  • Multiperiod sampling
  • Random processes
  • Retrospective sampling
  • Sampling methods
  • Social welfare data estimation methods
  • Stratified sampling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Science Applications
  • Computational Theory and Mathematics

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