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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.'
address: 'Centre for Mathematical Sciences, Box 118 Lund University, SE-22100 Lund, Sweden'
author:
- Umberto Picchini
- Rachele Anderson
bibliography:
- 'biblio.bib'
title: 'Approximate maximum likelihood estimation using data-cloning ABC'
---

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approximate Bayesian computation ,intractable likelihood ,MCMC ,state-space model ,stochastic differential equation

Introduction
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We present a methodology for approximate maximum likelihood 