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

This article considers the problem of modeling a class of count time series with multiple
change-points using segmented generalized integer-valued autoregressive (S-GINAR) …

Count data frequently exhibit overdispersion, zero inflation and even heavy-tailedness (the
tail probabilities are non-negligible or decrease very slowly) in practical applications. Many …

Integer-valued autoregressive (INAR) processes are generally defined by specifying the
thinning operator and either the innovations or the marginal distributions. The major …

Mixed Poisson distributions are a class of distributions arising from the Poisson mean
fluctuating as a random variable. Mixed Poisson distributions have been applied in diverse …

In this paper, we introduce a first-order integer-valued autoregressive process with zero-
modified Poisson-Lindley distributed innovations based on the binomial thinning operator …

This paper proposes a novel Bivariate integer-valued auto-regressive model of order 1 with
paired Poisson Weighted Exponential (PWE) distributed innovations which is denoted by …

Count data with zero truncation are common in the production process. It's essential to
monitor these data during production flow, production quality control and market …

The first-order binomial autoregressive (BAR (1)) model is the most frequently used tool to
analyze the bounded count time series. The BAR (1) model is stationary and assumes …

To better describe the data whose variance is greater than mean in time series analysis, this
paper introduces the RCINAR (1) process with generalized negative binomial marginals …