A much-needed introduction to the field of discrete-valued time series, with a focus on count-
data time series Time series analysis is an essential tool in a wide array of fields, including …

Abstract During the last 20–30 years, there was a remarkable growth in interest on
approaches for stationary count time series. We consider popular classes of models for such …

Abstract Markov Chain Monte Carlo (MCMC) methods have been shown to be a useful tool
in many branches in statistics. However, due to the complex structure of the models, this …

In this article, we introduce a new random coefficients self-exciting threshold integer-valued
autoregressive process. The autoregressive coefficients are driven by a logistic regression …

Zero inflation, zero deflation, overdispersion, and underdispersion are commonly
encountered in count time series. To better describe these characteristics of counts, this …

This paper introduces an observation-driven integer-valued time series model, in which the
underlying generating stochastic process is binomially distributed conditional on past …

This paper introduces two classes of binomial integer-valued ARCH models with dynamic
survival probabilities, each of which is controlled by a stochastic recurrence equation …

In this paper, we introduce a first-order random coefficient integer-valued threshold
autoregressive process, which is based on binomial thinning. Basic probabilistic and …

To capture the bivariate count time series showing piecewise phenomena, we introduce a
first-order bivariate threshold Poisson integer-valued autoregressive process. Basic …

Real count data time series often show an excessive number of zeros, which can form quite
different patterns. We develop four extensions of the binomial autoregressive model for …