Statistical models for autocorrelated count data

Statistics in Medicine

Volumn 25, Issue 8, 2006, Pages 1413-1430

  Nelson, Kerrie P ^a   Leroux, Brian G ^b  

^a UNIVERSITY OF SOUTH CAROLINA   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Generalized linear mixed model; Iterative bias correction; Maximum likelihood; Penalized quasi likelihood; Polio incidence

Indexed keywords

ALGORITHM; ANALYTICAL ERROR; ARTICLE; BAYES THEOREM; CORRELATION ANALYSIS; HUMAN; INCIDENCE; MAXIMUM LIKELIHOOD METHOD; METHODOLOGY; MONTE CARLO METHOD; POLIOMYELITIS; PREDICTION; PROBABILITY; SIMULATION; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; UNITED STATES;

ALGORITHMS; BAYES THEOREM; BIOMETRY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; HUMANS; INCIDENCE; LIKELIHOOD FUNCTIONS; LINEAR MODELS; MARKOV CHAINS; MONTE CARLO METHOD; POLIOMYELITIS; SOFTWARE; STOCHASTIC PROCESSES; UNITED STATES;

EID: 33646151838     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2274     Document Type: Article

References (20)

1. Zeger SL. A regression model for time series of counts. Biometrika 1988; 75:621-629.
2. Centers for Disease Control. Morbidity and Mortality Weekly Report Annual Summary: Reported Morbidity and Mortality in the U.S. 1970-1983; 18-31(54).
3. Chan KS, Ledolter J. Monte-Carlo EM estimation for time-series models involving counts. Journal of the American Statistical Association 1995; 92(429):242-252.
4. McCulloch CE. Maximum likelihood algorithms for generalized linear mixed models. Journal of the American Statistical Association 1997; 92(437):162-170.
5. Kuk AYC, Cheng YW. The Monte Carlo Newton-Raphson algorithm. Journal of Statistical Computation and Simulation 1997; 59:233-350.
6. Kuk AYC, Cheng YW. Pointwise and functional approximations in Monte Carlo maximum likelihood estimation. Statistics and Computing 1999; 9:91-99.
7. Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 1993; 88(421):9-25.
8. Oh MS, Lim YB. Bayesian analysis of time series Poisson data. Journal of Applied Statistics 2001; 28(2): 259-271.
9. Pinheiro JC, Bates DM. Approximations to the loglikelihood function in the nonlinear mixed effects model. Journal of Computational and Graphical Statistics 1995; 4:12-35.
10. Kuk AYC. Asymptotically unbiased estimation in generalized linear models with random effects. Journal of the Royal Statistical Society, Series B 1995; 57:395-407.
11. Nelson KP. Generalized linear mixed models: development and comparison of different estimation methods. Ph.D. Thesis, Department of Statistics, University of Washington, Seattle, Washington, 2002.
12. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear mixed models. Biometrika 1986; 73:13-22.
13. McCullagh P. Quasi-likelihood functions. Annals of Statistics 1983; 11:59-67.
14. McGilchrist CA. Estimation in generalized mixed models. Journal of the Royal Statistical Society, Series B 1994; 56:61-69.
15. Engel B, Keen A. A simple approach for the analysis of generalized linear mixed models. Statistica Neerlandica 1994; 48:1-22.
16. Schall R. Estimation in generalized linear models with random effects. Biometrika 1991; 40:719-727.
17. Wolfinger R, O'Connell M. Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 1993; 48:233-243.
18. Centre for Multilevel Modelling. MLwiN. Institute of Education: London, UK.
19. Hoeschele I, Tier B. Estimation of variance components of threshold characters by marginal posterio modes and means via Gibbs sampling. Genetics Selection Evolution 1995; 27:519-540.
20. Lin X, Breslow NE. Bias correction in generalized linear mixed models with multiple components of dispersion. Journal of the American Statistical Association 1996; 91:1007-1016.