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Title: Parameter estimation for hidden markov models with intractable likelihoods
Authors: Dean, T.A.
Singh, S.S.
Jasra, A. 
Peters, G.W.
Keywords: Approximate Bayesian computation
Hidden Markov model
Parameter estimation
Sequential Monte Carlo
Issue Date: 2014
Citation: Dean, T.A., Singh, S.S., Jasra, A., Peters, G.W. (2014). Parameter estimation for hidden markov models with intractable likelihoods. Scandinavian Journal of Statistics. ScholarBank@NUS Repository.
Abstract: Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures. © 2014 Board of the Foundation of the Scandinavian Journal of Statistics.
Source Title: Scandinavian Journal of Statistics
ISSN: 03036898
DOI: 10.1111/sjos.12077
Appears in Collections:Staff Publications

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