Higher-Order INAR Model Based on a Flexible Innovation and Application to COVID-19 and Gold Particles Data-Reference-Cited by-同舟云学术

Higher-Order INAR Model Based on a Flexible Innovation and Application to COVID-19 and Gold Particles Data

Published:2023-12-31 Issue:1 Volume:13 Page:32
ISSN:2075-1680
Container-title:Axioms
language:en
Short-container-title:Axioms

Author:

Almuhayfith Fatimah E.¹^ORCID,Krishna Anuresha²,Maya Radhakumari³^ORCID,Irshad Muhammad Rasheed²^ORCID,Bakouch Hassan S.⁴⁵^ORCID,Almulhim Munirah¹

Affiliation:

1. Department of Mathematics and Statistics, College of Science, King Faisal University, Alahsa 31982, Saudi Arabia

2. Department of Statistics, Cochin University of Science and Technology, Cochin 682022, India

3. Department of Statistics, University College, Thiruvananthapuram 695034, India

4. Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia

5. Department of Mathematics, Faculty of Science, Tanta University, Tanta 31111, Egypt

Abstract

INAR models have the great advantage of being able to capture the conditional distribution of a count time series based on their past observations, thus allowing it to be tailored to meet the unique characteristics of count data. This paper reviews the two-parameter Poisson extended exponential (PEE) distribution and its corresponding INAR(1) process. Then the INAR of order p (INAR(p)) model that incorporates PEE innovations is proposed, its statistical properties are presented, and its parameters are estimated using conditional least squares and conditional maximum likelihood estimation methods. Two practical data sets are analyzed and compared with competing INAR models in an effort to gauge the performance of the proposed model. It is found that the proposed model performs better than the competitors.

Publisher

MDPI AG

Subject

Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis

Link

https://www.mdpi.com/2075-1680/13/1/32/pdf

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