[Al-Osh, M. A. and Alzaid, A. A. 1988. Integer-valued moving average (inma) process. Statistical Papers 29, 281–300.]Search in Google Scholar
[Alzaid, A. and Al Osh, M. 1990. An integer-valued pth order autoregressive structure (INAR(p)) process. Journal of Applied 27, 314–324.]Search in Google Scholar
[Castillo, J. and Perez-Cassany, M. 2005. Overdispersed and underdispersed Poisson generalizations. Journal of Statistical Planning and Inference 134, 486–500.]Search in Google Scholar
[Chan, K. and Ledolter, J. 1995. Monte carlo EM estimation for time series models involving counts. Journal of the American Statistical Association 90(429), 242–252.]Search in Google Scholar
[Consul, P. and Shoukri, M. 1984. Maximum likelihood estimation for the generalized Poisson distribution. Communication in Statistics, Theory and Methodology 17, 1533–1547.]Search in Google Scholar
[Cox, D. 1981. Statistical analysis of time series: Some recent developments. Scandinavian Journal of Statistics 8(2), 93–115.]Search in Google Scholar
[Crowder, M. 1995. On the use of a working correlation matrix in using generalized linear models for repeated measures. Biometrika 82, 407–10.]Search in Google Scholar
[Dey, S., Dey, T., Ali, S., and Mulekar, M. 2016. Two-parameter Maxwell distribution: Properties and different methods of estimation. Journal of Statistical Theory and Practice 10, 2, 291–310.]Search in Google Scholar
[Durbin, J. and Koopman, S. 2000. Time series analysis of non-gaussian observations based on state space models from both classical and bayesian perspectives. Journal of the Royal Statistical Society B62, 3–56.]Search in Google Scholar
[Guikema, S. and Coffelt, J. 2008. A flexible count data regression model for risk analysis. Risk Analysis 28, 213–223.]Search in Google Scholar
[Jung, R., Kukuk, M., and Tremayne, A. 2006. Time series of count data:, modelling, estimation and diagnostics. Computational Statistics and Data Analysis 51(4), 2350–2364.]Search in Google Scholar
[Jung, R. and Liesenfeld, R. 2001. Estimating time series models for count data using efficient importance sampling. Allgemeines Statistical Archives 85(4), 387–407.]Search in Google Scholar
[Jung, R. and Tremayne, A. 2003. Testing for serial dependence in time series models of counts. Journal of Time Series Analysis 24, 1, 65–84.]Search in Google Scholar
[Liang, K. and Zeger, S. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22.]Search in Google Scholar
[Lord, D., Geedipally, S., and Guikema, S. 2010. Extension of the application of Conway-Maxwell Poisson models:analysing traffic crash data exhibiting underdispersion. Risk Analysis 30, 1268–1276.]Search in Google Scholar
[Mamode khan, N. and Jowaheer, V. 2013. Comparing joint GQL estimation and GMM adaptive estimation in COM-Poisson longitudinal regression model. Commun Stat-Simul C. 42(4), 755–770.]Search in Google Scholar
[Mamode khan, N., Sunecher, Y., and Jowaheer, V. 2016. Modelling a non-stationary BINAR(1) Poisson process. Journal of Statistical Computation and Simulation 86, 3106–3126.]Search in Google Scholar
[McKenzie, E. 1986. Autoregressive moving-average processes with Negative Binomial and geometric marginal distributions. Advanced Applied Probability 18, 679–705.]Search in Google Scholar
[Minka, T., Shmueli, G., Kadane, J., Borle, S., and Boatwright, P. 2003. Computing with the COM-Poisson distribution. Tech. rep., Carnegie Mellon University.]Search in Google Scholar
[Nadarajah, S. 2009. Useful moment and CDF formulations for the com-poisson distribution. Statistical papers 50, 617–622.]Search in Google Scholar
[Qu, A. and Lindsay, B. 2003. Building adaptive estimating equations when inverse of covariance estimation is difficult. Journal of Royal Statistical Society 65, 127–142.]Search in Google Scholar
[Quoreshi, A. 2008. A vector integer-valued moving average model for high frequency financial count data. Economics Letters 101, 258–261.]Search in Google Scholar
[Ravishanker, N., Serhiyenko, V., and Willig, M. 2014. Hierarchical dynamic models for multivariate time series of counts. Statistics and Its Interface 7, 559–570.]Search in Google Scholar
[Sellers, K., Morris, D., and Balakrishnan, N. 2016. Bivariate Conway-Maxwell Poisson distribution: Formulation, properties and inference. Journal of Multivariate Analysis 150, 152–168.]Search in Google Scholar
[Sellers, K., S.Borle, and Shmueli, G. 2012. The COM-Poisson model for count data: A survey of methods and applications. Applied Stochastic Models in Business and Industry, 22, 104–116.]Search in Google Scholar
[Shmueli, G., Minka, T., Borle, J., and Boatwright, P. 2005. A useful distribution for fitting discrete data. Applied Statistics,Journal of Royal Statistical Society C.]Search in Google Scholar
[Steutel, F. and Van Harn, K. 1979. Discrete analogues of self-decomposability and stability. The Annals of Probability 7, 3893–899.]Search in Google Scholar
[Sutradhar, B. 2003. An overview on regression models for discrete longitudinal responses. Statistical Science 18, 3, 377–393.]Search in Google Scholar
[Sutradhar, B. and Das, K. 1999. On the efficiency of regression estimators in generalised linear models for longitudinal data. Biometrika 86, 459–65.]Search in Google Scholar
[Sutradhar, B., Jowaheer, V., and Rao, P. 2014. Remarks on asymptotic efficient estimation for regression effects in stationary and non-stationary models for panel count data. Brazilian Journal of Probability and Statistics 28(2), 241–254.]Search in Google Scholar
[Wedderburn, R. 1974. Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika 61, 3 (December), 439–47.]Search in Google Scholar
