To capture the multivariate count time series showing piecewise phenomena, we introduce
a class of first-order multivariate threshold integer-valued autoregressive process. The …

Learning causal structure among event types from discrete-time event sequences is a
particularly important but challenging task. Existing methods, such as the multivariate …

When modelling unbounded counts, their marginals are often assumed to follow either
Poisson (Poi) or negative binomial (NB) distributions. To test such null hypotheses, we …

This paper proposes a first-order geometric autoregressive model based on a new thinning
operator. The parameters of the model are estimated by the method of Two-Stage …

Geometrically distributed counts arise in the industry. Ideally, they should be monitored
using a control chart whose average run length (ARL) function achieves a maximum when …

This article focuses on the coherent forecasting of the recently introduced novel geometric
AR (1)(NoGeAR (1)) model-an INAR model based on inflated-parameter binomial thinning …

In order to understand the behavior of disease transmission and develop better policies to
overcome the problem, time series modeling and forecasting of infectious diseases are …

The literature on discrete valued time series is expanding very fast. Very often we see new
models with very similar properties to the existing ones. A natural question that arises is …

The geometric first-order integer-valued autoregressive process (GINAR (1)) can be
particularly useful to model relevant discrete-valued time series, namely in statistical process …

This paper proposes a new flexible discrete triplet Lindley model that is constructed from the
balanced discretization principle of the extended Lindley distribution. This model has …