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Conditional Iterative Proportional Fitting for Gaussian Distributions


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  • Cramer, Erhard
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    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.

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 65 (1998)
    Issue (Month): 2 (May)
    Pages: 261-276

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    Handle: RePEc:eee:jmvana:v:65:y:1998:i:2:p:261-276

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    Keywords: Conditional iterative proportional fitting; iterative proportional fitting; I-projection; Kullback-Leibler distance; distributions with given marginals; conditional specification; Gaussian distribution;


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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. 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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