@article { ISI:000239509800005,
	title = {Modelling count data time series with Markov processes based on binomial thinning},
	journal = {Journal of Time Series Analysis},
	volume = {27},
	number = {5},
	year = {2006},
	month = {SEP},
	pages = {725-738},
	publisher = {Blackwell Publishing},
	type = {Article},
	abstract = {We obtain new models and results for count data time series based on binomial thinning. Count data time series may have non-stationarity from trends or covariates, so we propose an extension of stationary time series based on binomial thinning such that the univariate marginal distributions are always in the same parametric family, such as negative binomial. We propose a recursive algorithm to calculate the probability mass functions for the innovation random variable associated with binomial thinning. This simplifies numerical calculations and estimation for the classes of time series models that we consider. An application with real data is used to illustrate the models.},
	keywords = {Binomial thinning, count data time series, discrete self-decomposability, higher order Markov dependence, overdispersion, time-varying mean},
	issn = {0143-9782},
	doi = {10.1111/j.1467-9892.2006.00485.x},
	author = {Zhu, Rong and Joe, Harry}
}
