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Register a new userLikelihood-free stochastic approximation EM for inference in complex models
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Umberto Picchini
Publication date
2016
fields
Mathematical Statistics
and research's language is
English
Authors
Umberto Picchini
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A maximum likelihood methodology for the parameters of models with an intractable likelihood is introduced. We produce a likelihood-free version of the stochastic approximation expectation-maximization (SAEM) algorithm to maximize the likelihood function of model parameters. While SAEM is best suited for models having a tractable complete likelihood function, its application to moderately complex models is a difficult or even impossible task. We show how to construct a likelihood-free version of SAEM by using the synthetic likelihood paradigm. Our method is completely plug-and-play, requires almost no tuning and can be applied to both static and dynamic models. Four simulation studies illustrate the method, including a stochastic differential equation model, a stochastic Lotka-Volterra model and data from $g$-and-$k$ distributions. MATLAB code is available as supplementary material.
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