Likelihood-free stochastic approximation EM for inference in complex models
(2019) In Communications in Statistics: Simulation and Computation 48(3). p.861-881- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b4e72775-a092-4b85-9ccc-cc3694998fab
- author
- Picchini, Umberto LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- maximum likelihood, SAEM, sequential Monte Carlo, synthetic likelihood;, state space model, Stochastic differential equation
- in
- Communications in Statistics: Simulation and Computation
- volume
- 48
- issue
- 3
- pages
- 26 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85041005919
- ISSN
- 0361-0918
- DOI
- 10.1080/03610918.2017.1401082
- project
- Stochastic modelling of protein folding and likelihood-free statistical inference methods
- language
- English
- LU publication?
- yes
- id
- b4e72775-a092-4b85-9ccc-cc3694998fab
- alternative location
- https://arxiv.org/abs/1609.03508
- date added to LUP
- 2016-09-13 10:51:54
- date last changed
- 2022-04-25 05:12:10
@article{b4e72775-a092-4b85-9ccc-cc3694998fab,
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.}},
author = {{Picchini, Umberto}},
issn = {{0361-0918}},
keywords = {{maximum likelihood; SAEM; sequential Monte Carlo; synthetic likelihood;; state space model; Stochastic differential equation}},
language = {{eng}},
number = {{3}},
pages = {{861--881}},
publisher = {{Taylor & Francis}},
series = {{Communications in Statistics: Simulation and Computation}},
title = {{Likelihood-free stochastic approximation EM for inference in complex models}},
url = {{http://dx.doi.org/10.1080/03610918.2017.1401082}},
doi = {{10.1080/03610918.2017.1401082}},
volume = {{48}},
year = {{2019}},
}