Approximate maximum likelihood estimation using data-cloning ABC
Picchini, Umberto LU and Anderson, Rachele LU
(2017)
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:
https://lup.lub.lu.se/record/8410635
- author
- Picchini, Umberto
LU
and Anderson, Rachele
LU
- organization
- publishing date
- 2017
- 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
- wos:000385604500012
- ISSN
- 0167-9473
- DOI
- 10.1016/j.csda.2016.08.006
- project
- Stochastic modelling of protein folding and likelihood-free statistical inference methods
- 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
- 2016-04-04 14:19:24
- date last changed
- 2022-02-21 20:18:44
@article{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}},
keywords = {{Approximate Bayesian computation; Intractable likelihood; MCMC; State-space model; Stochastic differential equation}},
language = {{eng}},
pages = {{166--183}},
publisher = {{Elsevier}},
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}},
doi = {{10.1016/j.csda.2016.08.006}},
volume = {{105}},
year = {{2017}},
}