Likelihood-free stochastic approximation EM for inference in complex models

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Bibtex

@article{b4e72775a0924b859ccccc3694998fab,
title = "Likelihood-free stochastic approximation EM for inference in complex models",
abstract = "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.",
keywords = "maximum likelihood, SAEM, sequential Monte Carlo, synthetic likelihood;, state space model, Stochastic differential equation",
author = "Umberto Picchini",
year = "2019",
doi = "10.1080/03610918.2017.1401082",
language = "English",
volume = "48",
pages = "861--881",
journal = "Communications in Statistics Part B: Simulation and Computation",
issn = "0361-0918",
publisher = "Taylor & Francis",
number = "3",

}