High-order self-excited multiple thresholds generalized integer-valued autoregressive model

This work proposes an integer threshold autoregressive model with multiple regimes $\left( K\geq2\right)$, based on a generalized thinning operator (hereafter referred to as $SET-GINAR\left( K;p\right)$). This model will be useful for analyzing the number of certain arrivals in a fixed time interval with non-linear behavior. First, we study the probabilistic structure of our model through the stationarity issue and the moments structure. Second, we provide two statistical inference procedures, namely two estimation methods including the conditional least squares and the conditional maximum likelihood. In addition, the asymptotic properties of the estimators, including consistency and normality, are established. Finally, the performance of the obtained inference procedures will be evaluated through an intensive simulation study and application on real data.

Authors

Institutions

Publication Details

Journal
Hacettepe Journal of Mathematics and Statistics
Published
2025-08-25
DOI
https://doi.org/10.15672/hujms.1650968
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High-order self-excited multiple thresholds generalized integer-valued autoregressive model

Mohamed Sadoun
Hacettepe Journal of Mathematics and Statistics
Neural Networks and Applications
article

High-order self-excited multiple thresholds generalized integer-valued autoregressive model

Mohamed Sadoun
article en

Abstract

This work proposes an integer threshold autoregressive model with multiple regimes $\left( K\geq2\right)$, based on a generalized thinning operator (hereafter referred to as $SET-GINAR\left( K;p\right)$). This model will be useful for analyzing the number of certain arrivals in a fixed time interval with non-linear behavior. First, we study the probabilistic structure of our model through the stationarity issue and the moments structure. Second, we provide two statistical inference procedures, namely two estimation methods including the conditional least squares and the conditional maximum likelihood. In addition, the asymptotic properties of the estimators, including consistency and normality, are established. Finally, the performance of the obtained inference procedures will be evaluated through an intensive simulation study and application on real data.

Hacettepe Journal of Mathematics and StatisticsVol. 54(5)
University of Sciences and Technology Houari Boumediene (DZ)
Openalex Percentile: Top 89%
Neural Networks and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.