Paper Info
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.
Year
DOI
Venue
2019
10.1080/03610918.2017.1401082
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Keywords
Field
DocType
Incomplete data,Intractable likelihood,SAEM,State-space model,Synthetic likelihood
Econometrics,Likelihood function,Inference,Particle filter,State-space representation,Maximum likelihood,Stochastic differential equation,Dynamic models,Statistics,Stochastic approximation,Mathematics
Journal
Volume
Issue
ISSN
48.0
3.0
0361-0918
Citations 
PageRank 
References 
0
0.34
6
Authors
1
Name
Order
Citations
PageRank
Umberto Picchini192.99