We review regression models for count time series. We discuss the approach that is based
on generalized linear models and the class of integer autoregressive processes. The …

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 …

Abstract Approximate Bayesian Computation (ABC) has become increasingly prominent as
a method for conducting parameter inference in a range of challenging statistical problems …

This paper examines a bivariate count time series with some curious statistical features:
Saffir–Simpson Category 3 and stronger annual hurricane counts in the North Atlantic and …

Efficient probabilistic forecasts of integer-valued random variables are derived. The
optimality is achieved by estimating the forecast distribution non-parametrically over a given …

We propose a dynamic factor model for the analysis of multivariate time series count data.
Our model allows for idiosyncratic as well as common serially correlated latent factors in …

In this article, we present an overview of recent advances in Bayesian modeling and
analysis of multivariate time series of counts. We discuss basic modeling strategies …

Linear processes have been one of the most fundamental tools in modeling serially
dependent data. These models and methods heavily depend on Gaussian processes and …

In this study, Bayesian estimation is performed for a class of random coefficient self-exciting
integer-valued threshold autoregressive processes with explanatory variables. A new model …

This paper addresses aggregation in integer autoregressive moving average (INARMA)
models. Although aggregation in continuous-valued time series has been widely discussed …