Dean, TA and Singh, SS and Jasra, A and Peters, GW (2014) Parameter Estimation for Hidden Markov Models with Intractable Likelihoods. Scandinavian Journal of Statistics. n/a-n/a. ISSN 1467-9469Full text not available from this repository.
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.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:31|
|Last Modified:||24 Apr 2017 03:59|