Count time series are widely available in fields such as epidemiology, finance, meteorology,
and sports, and thus there is a growing demand for both methodological and application …

Numerous models have been proposed for count time series, including the integer-valued
autoregressive moving average (ARMA) and integer-valued generalized autoregressive …

Time series of counts observed in practice often exhibit overdispersion or underdispersion,
zero inflation and even heavy-tailedness (the tail probabilities are non-negligible or …

In this article, we introduce a multiplicative integer‐valued time series model, which is
defined as the product of a unit‐mean integer‐valued independent and identically …

Recently, there has been a growing interest in integer-valued time series models, including
integer-valued autoregressive (INAR) models and integer-valued generalized …

We propose a novel flexible bivariate conditional Poisson (BCP) INteger‐valued
Generalized AutoRegressive Conditional Heteroscedastic (INGARCH) model for correlated …

In statistical diagnostics, detecting influential observations is pivotal for assessing model
fitting. To address parameter restrictions while maintaining necessary properties, the …

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model
for non-stationary count time series is proposed. The non-stationarity of the bivariate count …

We propose a novel class of first-order integer-valued AutoRegressive (INAR (1)) models
based on a new operator, the so-called geometric thinning operator, which induces a certain …

The article presents a robust quantitative approach for determining significant economic
factors for sex trafficking in the United States. The aim is to study monthly counts of sex …