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Theory and Method

Monte Carlo EM Estimation for Time Series Models Involving Counts

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Pages 242-252 | Received 01 Sep 1992, Published online: 27 Feb 2012

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Crossref
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Stanley Xu, Richard H. Jones & Gary K. Grunwald. (2007) Analysis of Longitudinal Count Data with Serial Correlation. Biometrical Journal 49:3, pages 416-428.
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Crossref
Kerrie P. Nelson & Brian G. Leroux. (2006) Statistical models for autocorrelated count data. Statistics in Medicine 25:8, pages 1413-1430.
Crossref
Florin VaidaXiao-Li Meng. (2016) Two slice-EM algorithms for fitting generalized linear mixed models with binary response. Statistical Modelling 5:3, pages 229-242.
Crossref
Perry de Valpine & Ray Hilborn. (2005) State-space likelihoods for nonlinear fisheries time-series. Canadian Journal of Fisheries and Aquatic Sciences 62:9, pages 1937-1952.
Crossref
Dimitris Karlis. (2015) EM Algorithm for Mixed Poisson and Other Discrete Distributions. ASTIN Bulletin 35:1, pages 3-24.
Crossref
P. J. Brockwell. 2005. Encyclopedia of Statistics in Behavioral Science. Encyclopedia of Statistics in Behavioral Science.
Lang Wu. (2008) Nonlinear mixed‐effect models with nonignorably missing covariates. Canadian Journal of Statistics 32:1, pages 27-37.
Crossref
Miguel López-Díaz, María Á. Gil, Przemysław Grzegorzewski, Olgierd Hryniewicz & Jonathan LawryCarlos Carleos, Norberto Corral, M. Asunción Lubiano & Jesús Angel Baro. 2004. Soft Methodology and Random Information Systems. Soft Methodology and Random Information Systems 693 700 .
Brent A CoullAlan Agresti. (2016) Generalized log-linear models with random effects, with application to smoothing contingency tables. Statistical Modelling 3:4, pages 251-271.
Crossref
Natacha Brouhns, Montserrat Guillén, Michel Denuit & Jean Pinquet. (2003) Bonus‐Malus Scales in Segmented Tariffs With Stochastic Migration Between Segments. Journal of Risk and Insurance 70:4, pages 577-599.
Crossref
G. Fort, E. Moulines, G. O. Roberts & J. S. Rosenthal. (2016) On the geometric ergodicity of hybrid samplers. Journal of Applied Probability 40:1, pages 123-146.
Crossref
G. Fort, E. Moulines, G. O. Roberts & J. S. Rosenthal. (2016) On the geometric ergodicity of hybrid samplers. Journal of Applied Probability 40:01, pages 123-146.
Crossref
Eddie McKenzie. 2003. Stochastic Processes: Modelling and Simulation. Stochastic Processes: Modelling and Simulation 573 606 .
Hao Zhang. (2004) On Estimation and Prediction for Spatial Generalized Linear Mixed Models. Biometrics 58:1, pages 129-136.
Crossref
Rung-Ching Tsai & Ulf Böckenholt. (2001) Maximum Likelihood Estimation of Factor and Ideal Point Models for Paired Comparison Data. Journal of Mathematical Psychology 45:6, pages 795-811.
Crossref
Hisashi Tanizaki. (2001) Estimation of unknown parameters in nonlinear and non-Gaussian state-space models. Journal of Statistical Planning and Inference 96:2, pages 301-323.
Crossref
Siddhartha Chib. 2001. 3569 3649 .
Florin Vaida & Ronghui Xu. (2000) Proportional hazards model with random effects. Statistics in Medicine 19:24, pages 3309-3324.
Crossref
V. Solo. (2000) 'Unobserved' Monte Carlo method for identification of partially observed nonlinear state space systems. Part II. Counting process observations. 'Unobserved' Monte Carlo method for identification of partially observed nonlinear state space systems. Part II. Counting process observations.
Bent Jørgensen & Min Tsao. (1999) Dispersion models and longitudinal data analysis. Statistics in Medicine 18:17-18, pages 2257-2270.
Crossref
Bent Jørgensen, Søren Lundbye‐Christensen, Peter Xue‐Kun Song & Li Sun. (2008) State‐space models for multivariate longitudinal data of mixed types. Canadian Journal of Statistics 24:3, pages 385-402.
Crossref
Neil Shephard. 1996. Time Series Models. Time Series Models 1 67 .
Robert Phillips, A. Serdar Şimşek & Garrett van Ryzin. (2021) Predicting Transaction Outcomes Under Customized Pricing with Discretion: A Structural Estimation Approach. SSRN Electronic Journal.
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Patrick Gagliardini, Eric Ghysels & Mirco Rubin. (2016) Indirect Inference Estimation of Mixed Frequency Stochastic Volatility State Space Models Using MIDAS Regressions and ARCH Models. SSRN Electronic Journal.
Crossref
Drew D. Creal. (2013) A Class of Non-Gaussian State Space Models with Exact Likelihood Inference. SSRN Electronic Journal.
Crossref
Kay Giesecke & Gustavo Schwenkler. (2011) Filtered Likelihood for Point Processes. SSRN Electronic Journal.
Crossref
Benjamin J. Gillen. (2009) Identification and Estimation of Level-K Auctions. SSRN Electronic Journal.
Crossref

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