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Journal Article

Monte Carlo Methods of Inference for Implicit Statistical Models

Peter J. Diggle and Richard J. Gratton
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 46, No. 2 (1984), pp. 193-227
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2345504
Page Count: 35
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Monte Carlo Methods of Inference for Implicit Statistical Models
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Abstract

A prescribed statistical model is a parametric specification of the distribution of a random vector, whilst an implicit statistical model is one defined at a more fundamental level in terms of a generating stochastic mechanism. This paper develops methods of inference which can be used for implicit statistical models whose distribution theory is intractable. The kernel method of probability density estimation is advocated for estimating a log-likelihood from simulations of such a model. The development and testing of an algorithm for maximizing this estimated log-likelihood function is described. An illustrative example involving a stochastic model for quantal response assays is given. Possible applications of the maximization algorithm to ad hoc methods of parameter estimation are noted briefly, and illustrated by an example involving a model for the spatial pattern of displaced amacrine cells in the retina of a rabbit.

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