Zero-and-one inflated Poisson–Lindley INAR (1) process for modelling count time series with extra zeros and ones
In this paper, a first-order integer-valued autoregressive (INAR (1)) model with zero-and-one
inflated Poisson–Lindley distributed innovations is presented. It is shown that the model …
inflated Poisson–Lindley distributed innovations is presented. It is shown that the model …
A zero‐modified geometric INAR (1) model for analyzing count time series with multiple features
Y Kang, F Zhu, D Wang, S Wang - Canadian Journal of …, 2024 - Wiley Online Library
Zero inflation, zero deflation, overdispersion, and underdispersion are commonly
encountered in count time series. To better describe these characteristics of counts, this …
encountered in count time series. To better describe these characteristics of counts, this …
Testing for zero inflation and overdispersion in INAR (1) models
The marginal distribution of count data processes rarely follows a simple Poisson model in
practice. Instead, one commonly observes deviations such as overdispersion or zero …
practice. Instead, one commonly observes deviations such as overdispersion or zero …
Modeling zero inflation in count data time series with bounded support
TA Möller, C H. Weiß, HY Kim, A Sirchenko - Methodology and Computing …, 2018 - Springer
Real count data time series often show an excessive number of zeros, which can form quite
different patterns. We develop four extensions of the binomial autoregressive model for …
different patterns. We develop four extensions of the binomial autoregressive model for …
Integer-valued autoregressive processes with prespecified marginal and innovation distributions: a novel perspective
Integer-valued autoregressive (INAR) processes are generally defined by specifying the
thinning operator and either the innovations or the marginal distributions. The major …
thinning operator and either the innovations or the marginal distributions. The major …
Mixed poisson INAR (1) processes
W Barreto-Souza - Statistical papers, 2019 - Springer
Overdispersion is a phenomenon commonly observed in count time series. Since Poisson
distribution is equidispersed, the INteger-valued AutoRegressive (INAR) process with …
distribution is equidispersed, the INteger-valued AutoRegressive (INAR) process with …
A first-order integer-valued autoregressive process with zero-modified Poisson-Lindley distributed innovations
In this paper, we introduce a first-order integer-valued autoregressive process with zero-
modified Poisson-Lindley distributed innovations based on the binomial thinning operator …
modified Poisson-Lindley distributed innovations based on the binomial thinning operator …
Some developments on seasonal INAR processes with application to influenza data
Influenza epidemic data are seasonal in nature. Zero-inflation, zero-deflation,
overdispersion, and underdispersion are frequently seen in such number of cases of …
overdispersion, and underdispersion are frequently seen in such number of cases of …
A zero-inflated geometric INAR (1) process with random coefficient
HS Bakouch, M Mohammadpour… - Applications of …, 2018 - Springer
Many real-life count data are frequently characterized by overdispersion, excess zeros and
autocorrelation. Zero-inflated count time series models can provide a powerful procedure to …
autocorrelation. Zero-inflated count time series models can provide a powerful procedure to …
On two classes of reflected autoregressive processes
We introduce two general classes of reflected autoregressive processes, INGAR+ and
GAR+. Here, INGAR+ can be seen as the counterpart of INAR (1) with general thinning and …
GAR+. Here, INGAR+ can be seen as the counterpart of INAR (1) with general thinning and …