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The fitting of complex parametric models

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  • D. R. Cox
  • Christiana Kartsonaki

Abstract

Consider parametric models that are too complicated to allow calculation of a likelihood but from which observations can be simulated. We examine parameter estimators that are linear functions of a possibly large set of candidate features. A combination of simulations based on a fractional design and sets of discriminant analyses is then used to find an optimal estimator of the vector parameter and its covariance matrix. The procedure is an alternative to the approximate Bayesian computation scheme. Copyright 2012, Oxford University Press.

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  • D. R. Cox & Christiana Kartsonaki, 2012. "The fitting of complex parametric models," Biometrika, Biometrika Trust, vol. 99(3), pages 741-747.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:3:p:741-747
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    File URL: http://hdl.handle.net/10.1093/biomet/ass030
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    Cited by:

    1. Bee, Marco & Espa, Giuseppe & Giuliani, Diego, 2015. "Approximate maximum likelihood estimation of the autologistic model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 14-26.

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