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Testing lattice conditional independence models based on monotone missing data

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  • Wu, Lang
  • Perlman, Michael D.

Abstract

Lattice conditional independence (LCI) models (Anderson and Perlman, 1991. Statist. Probab. Lett. 12, 465-486; 1993 Ann. Statist. 21, 1318-1358) can be applied to the analysis of missing data problems with non-monotone missing patterns. Closed-form maximum likelihood estimates can always be obtained under the LCI models naturally determined by the observed data patterns. In practice, it is important to test the appropriateness of LCI models. In the present paper, we derive explicit likelihood ratio tests for testing LCI models based on a monotone subset of the observed data.

Suggested Citation

  • Wu, Lang & Perlman, Michael D., 2000. "Testing lattice conditional independence models based on monotone missing data," Statistics & Probability Letters, Elsevier, vol. 50(2), pages 193-201, November.
    • Handle: RePEc:eee:stapro:v:50:y:2000:i:2:p:193-201
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    References listed on IDEAS

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    1. Andersson, S. A. & Perlman, M. D., 1995. "Testing Lattice Conditional Independence Models," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 18-38, April.
    2. Andersson, Steen A. & Perlman, Michael D., 1991. "Lattice-ordered conditional independence models for missing data," Statistics & Probability Letters, Elsevier, vol. 12(6), pages 465-486, December.
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    Cited by:

    1. Richards, Donald St. P. & Yamada, Tomoya, 2010. "The Stein phenomenon for monotone incomplete multivariate normal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 657-678, March.

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