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Conditional Iterative Proportional Fitting for Gaussian Distributions

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  • Cramer, Erhard

Abstract

A Gaussian version of the iterative proportional fitting procedure (IFP-P) was applied by Speed and Kiiveri to solve the likelihood equations in graphical Gaussian models. The calculation of the maximum likelihood estimates can be seen as the problem to find a Gaussian distribution with prescribed Gaussian marginals. We extend the Gaussian version of the IPF-P so that additionally given conditionals of Gaussian type are taken into account. The convergence of both proposed procedures, called conditional iterative proportional fitting procedures (CIPF-P), is proved.

Suggested Citation

  • Cramer, Erhard, 1998. "Conditional Iterative Proportional Fitting for Gaussian Distributions," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 261-276, May.
    • Handle: RePEc:eee:jmvana:v:65:y:1998:i:2:p:261-276
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    References listed on IDEAS

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    1. Arnold, Barry C. & Castillo, Enrique & Sarabia, José María, 1996. "Specification of distributions by combinations of marginal and conditional distributions," Statistics & Probability Letters, Elsevier, vol. 26(2), pages 153-157, February.
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    Cited by:

    1. Gramer Erhard, 2000. "Probability Measures With Given Marginals And Conditionals: I-Projections And Conditional Iterative Proportional Fitting," Statistics & Risk Modeling, De Gruyter, vol. 18(3), pages 311-330, March.
    2. Kaiser, Mark S. & Cressie, Noel, 2000. "The Construction of Multivariate Distributions from Markov Random Fields," Journal of Multivariate Analysis, Elsevier, vol. 73(2), pages 199-220, May.

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