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STATISTICAL MATCHING BASED ON PROBABILISTIC CONDITIONAL INDEPENDENCE

  • Wang Jinfang
    Department of Mathematics and Informatics, Graduate School of Science, Chiba University
  • Jing Ping
    Faculty of Science, China University of Mining and Technology

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Abstract

A concept of matchability of survey data is introduced based on decompositions of the joint probability density functions. This definition of matchability naturally leads to restrictions on the joint distributions in the form of various conditional independence relations. The concept of partial matchability is defined as the global matchability with respect to a subset of the underlying variables. The global matchability does not imply partial matchability and vice versa, which constitutes part of Simpson's paradox. A numerical experiment is carried out to show possible merits of algorithms based on partial matchability. We also show numerically that when the ideal assumption of matchability holds only approximately, estimation accuracy is still guaranteed to some extent. Extension to the problem of matching three files is also briefly discussed.

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Details

  • CRID
    1390001204414948480
  • NII Article ID
    110007502778
  • NII Book ID
    AA10823693
  • DOI
    10.5183/jjscs.22.1_43
  • ISSN
    18811337
    09152350
  • Text Lang
    en
  • Data Source
    • JaLC
    • Crossref
    • CiNii Articles
    • KAKEN

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