Please use this identifier to cite or link to this item:
|Title:||Parameter estimation for hidden markov models with intractable likelihoods||Authors:||Dean, T.A.
|Keywords:||Approximate Bayesian computation
Hidden Markov model
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. https://doi.org/10.1111/sjos.12077||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/125060||ISSN:||03036898||DOI:||10.1111/sjos.12077|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.