The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Monte Carlo EM Estimation for Time Series Models Involving Counts
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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Convergence of Probability Measures
Billingsley, P. (1999) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316962 · Cited by 13,910
Savitzky-Golay Smoothing Filters
Press, W. H., & Teukolsky, S. A. (1990) · Computers in Physics · doi:10.1063/1.4822961 · Cited by 11,670
Convergence of Probability Measures
Kingman, J. F. C., & Billingsley, P. (1969) · Revue de l'Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402124 · Cited by 6,812
Maximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
Scandinavian Journal of Statistics
Rosén, B. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess2351.pub2 · Cited by 3,595
Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,452
On the Convergence Properties of the EM Algorithm
Wu, C. F. J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346060 · Cited by 3,269
Anderson, B. D. O., Moore, J. B., & Eslami, M. (1982) · IEEE Transactions on Systems Man and Cybernetics · Cited by 3,195
Finding the Observed Information Matrix When Using the EM Algorithm
Louis, T. A. (1982) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1982.tb01203.x · Cited by 2,229
Cited by
Beskos, A., Papaspiliopoulos, O., Roberts, G. O., & Fearnhead, P. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2006.00552.x · Cited by 349
State-space likelihoods for nonlinear fisheries time-series
Valpine, P. D., & Hilborn, R. (2005) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f05-116 · Cited by 69
Metadata
- Authors
- Chan, K. S., & Ledolter, J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1995-03
- Volume
- 90
- Issue
- 429
- Pages
- 242-252
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1975593377
- Cited by
- 275
- References
- 26
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- closed
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