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Approximate maximum likelihood estimation using data-cloning ABC

Picchini, Umberto LU and Anderson, Rachele LU (2016) In Computational Statistics & Data Analysis 105. p.166-183
Abstract
A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC sampler with so-called "data cloning" for maximum likelihood estimation. Accuracy of ABC methods relies on the use of a small threshold value for comparing simulations from the model and observed data. The proposed methodology shows how to use large threshold values, while the number of data-clones is increased to ease convergence towards an approximate maximum likelihood estimate. We show how to exploit the methodology to reduce the number of iterations of a standard ABC-MCMC algorithm and therefore reduce... (More)
A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC sampler with so-called "data cloning" for maximum likelihood estimation. Accuracy of ABC methods relies on the use of a small threshold value for comparing simulations from the model and observed data. The proposed methodology shows how to use large threshold values, while the number of data-clones is increased to ease convergence towards an approximate maximum likelihood estimate. We show how to exploit the methodology to reduce the number of iterations of a standard ABC-MCMC algorithm and therefore reduce the computational effort, while obtaining reasonable point estimates. Simulation studies show the good performance of our approach on models with intractable likelihoods such as g-and-k distributions, stochastic differential equations and state-space models. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Approximate Bayesian computation, Intractable likelihood, MCMC, State-space model, Stochastic differential equation
in
Computational Statistics & Data Analysis
volume
105
pages
18 pages
publisher
Elsevier
external identifiers
  • Scopus:84986575881
ISSN
0167-9473
DOI
10.1016/j.csda.2016.08.006
language
English
LU publication?
yes
id
d367ad5c-a1ff-4d12-ad3c-f3796b135bdc (old id 8410635)
alternative location
http://arxiv.org/abs/1505.06318
date added to LUP
2016-02-29 16:14:36
date last changed
2016-10-03 07:24:53
@misc{d367ad5c-a1ff-4d12-ad3c-f3796b135bdc,
  abstract     = {A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods are models with intractable likelihoods, and we combine an ABC-MCMC sampler with so-called "data cloning" for maximum likelihood estimation. Accuracy of ABC methods relies on the use of a small threshold value for comparing simulations from the model and observed data. The proposed methodology shows how to use large threshold values, while the number of data-clones is increased to ease convergence towards an approximate maximum likelihood estimate. We show how to exploit the methodology to reduce the number of iterations of a standard ABC-MCMC algorithm and therefore reduce the computational effort, while obtaining reasonable point estimates. Simulation studies show the good performance of our approach on models with intractable likelihoods such as g-and-k distributions, stochastic differential equations and state-space models. },
  author       = {Picchini, Umberto and Anderson, Rachele},
  issn         = {0167-9473},
  keyword      = {Approximate Bayesian computation,Intractable likelihood,MCMC,State-space model,Stochastic differential equation},
  language     = {eng},
  month        = {08},
  pages        = {166--183},
  publisher    = {ARRAY(0x80e1de8)},
  series       = {Computational Statistics & Data Analysis},
  title        = {Approximate maximum likelihood estimation using data-cloning ABC},
  url          = {http://dx.doi.org/10.1016/j.csda.2016.08.006},
  volume       = {105},
  year         = {2016},
}