Approximate maximum likelihood estimation using data-cloning ABC

Umberto Picchini and Rachele Anderson

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 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 is 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.

Keywords: Approximate Bayesian computation; Intractable likelihood; MCMC; State-space model; Stochastic differential equation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:166-183

DOI: 10.1016/j.csda.2016.08.006

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