A growing interest in non-Gaussian time series, particularly in series comprised of
nonnegative integers (counts), is taking place in today's statistics literature. Count series …

The prequel to this review provided an extensive treatment of classic zero‐inflated count
regression models where a univariate discrete distribution is used for the count regression …

Discrete count time series data with an excessive number of zeros have warranted the
development of zero‐inflated time series models to incorporate the inflation of zeros and the …

In this work, we present a new generalized autoregressive moving average model
(GARMA), based on the Bernoulli-geometric (BerG) distribution, for modeling the conditional …

This book provides statistical methodologies for time series data, focusing on copula-based
Markov chain models for serially correlated time series. It also includes data examples from …

Time series analysis plays a vital role in modeling historical crash trends and predicting the
possible changes in future crash trends. In existing safety literature, earlier studies employed …

Count time series data with excess zeros are observed in several applied disciplines. When
these zero-inflated counts are sequentially recorded, they might result in serial dependence …

The ability of government agencies to assign accurate ages of fish is important to fisheries
management. Accurate ageing allows for most reliable age‐based models to be used to …

A copula-based Markov chain model can flexibly capture serial dependence in a time series.
However, the computational developments for copula-based Markov models remain …

The class of bivariate integer-valued time series models, described via copula theory, is
gaining popularity in the literature because of applications in health sciences, engineering …