Journal article
Monte Carlo EM Estimation for Time Series Models Involving Counts
Journal of the American Statistical Association, Vol.90(429), pp.242-252
03/1995
DOI: 10.1080/01621459.1995.10476508
Abstract
The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in very complex likelihoods, and even the EM algorithm, which is usually well suited for problems of this type, involves high-dimensional integration. In this article we discuss a Monte Carlo EM (MCEM) algorithm that uses a Markov chain sampling technique in the calculation of the expectation in the E step of the EM algorithm. We propose a stopping criterion for the algorithm and provide rules for selecting the appropriate Monte Carlo sample size. We show that under suitable regularity conditions, an MCEM algorithm will, with high probability, get close to a maximizer of the likelihood of the observed data. We also discuss the asymptotic efficiency of the procedure. We illustrate our Monte Carlo estimation method on a time series involving small counts: the polio incidence time series previously analyzed by Zeger. © 1995 Taylor & Francis Group, LLC.
Details
- Title: Subtitle
- Monte Carlo EM Estimation for Time Series Models Involving Counts
- Creators
- K. S. Chan - University of IowaJohannes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the American Statistical Association, Vol.90(429), pp.242-252
- DOI
- 10.1080/01621459.1995.10476508
- ISSN
- 0162-1459
- eISSN
- 1537-274X
- Language
- English
- Date published
- 03/1995
- Academic Unit
- Statistics and Actuarial Science; Radiology; Business Analytics
- Record Identifier
- 9984257629802771
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