Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process.

IF 2.6 3区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Jianhua Cheng, Xu Wang, Dehui Wang
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引用次数: 0

Abstract

In this paper, the authors consider the empirical likelihood method for a first-order generalized random coefficient integer-valued autoregressive process. The authors establish the log empirical likelihood ratio statistic and obtain its limiting distribution. Furthermore, the authors investigate the point estimation, confidence regions and hypothesis testing for the parameters of interest. The performance of empirical likelihood method is illustrated by a simulation study and a real data example.

一阶广义随机系数整值自回归过程的经验似然性。
本文研究了一类一阶广义随机系数整值自回归过程的经验似然方法。建立了对数经验似然比统计量,并得到了其极限分布。此外,作者还研究了感兴趣参数的点估计、置信区域和假设检验。通过仿真研究和实际数据算例说明了经验似然方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Systems Science & Complexity
Journal of Systems Science & Complexity 数学-数学跨学科应用
CiteScore
3.80
自引率
9.50%
发文量
90
审稿时长
6-12 weeks
期刊介绍: The Journal of Systems Science and Complexity is dedicated to publishing high quality papers on mathematical theories, methodologies, and applications of systems science and complexity science. It encourages fundamental research into complex systems and complexity and fosters cross-disciplinary approaches to elucidate the common mathematical methods that arise in natural, artificial, and social systems. Topics covered are: complex systems, systems control, operations research for complex systems, economic and financial systems analysis, statistics and data science, computer mathematics, systems security, coding theory and crypto-systems, other topics related to systems science.
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