Progressive random sampling: A multiperiod estimation technique with applications

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A number of applications, including claims made under Federal social welfare programs, require 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, then a method which is herein called progressive random sampling (PRS) may be applied. The proposed method serves to either improve sampling estimates or reduce sample sizes, as demonstrated by two example applications.

Original languageEnglish
Pages (from-to)418-426
Number of pages9
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume30
Issue number4
DOIs
StatePublished - Nov 1 2000
Externally publishedYes

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ASJC Scopus subject areas

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

Cite this

Progressive random sampling : A multiperiod estimation technique with applications. / De Los Santos, Plinio A.; Burke, Richard J.; Tien, James M.

In: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 30, No. 4, 01.11.2000, p. 418-426.

Research output: Contribution to journalArticle

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