In this article, we introduce a new random coefficients self-exciting threshold integer-valued
autoregressive process. The autoregressive coefficients are driven by a logistic regression …

The discrete minification model based on the modified negative binomial operator, as an
extension to the continuous minification model, can be used to describe an extreme value …

In the realm of time series data analysis, information criteria constructed on the basis of
likelihood functions serve as crucial instruments for determining the appropriate lag order …

In this paper we analyse some bootstrap techniques to make inference in INAR (p) models.
First of all, via Monte Carlo experiments we compare the performances of these methods …

The first-order Poisson autoregressive model may be suitable in situations where the time
series data are non-negative integer valued. In this article, we propose a new parameter …

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 …

In this paper, a new threshold INAR (1) model based on modified negative binomial operator
with random coefficient is proposed. Basic probabilistic and statistical properties of this …

In this article, a new random coefficient integer-valued self-exciting threshold autoregressive
process based on modified negative binomial operator is introduced. The autoregressive …

Non-negative integer-valued time series are usually encountered in practice, and a variety
of integer-valued autoregressive processes based on various thinning operators are …

A class of integer-valued autoregressive models is considered, which are based on a latten
process eg, a Markov chain. Some statistical properties of this class of models are …