多值膨胀的双变量 INAR 计数时间序列:以零一膨胀的泊松-林德利案例为特色

Pub Date : 2024-06-01 DOI:10.1007/s42952-024-00269-0
Sangyeol Lee, Minyoung Jo
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引用次数: 0

摘要

本研究考虑了多值膨胀双变量整数值自回归(MV-infflated BINAR)模型。它开发了该模型参数估计的推理程序,适用于构建变化点检验和离群点检测规则。我们首先介绍了具有单参数指数族和泊松-林德利创新的 MV 充气 BINAR 模型。然后,我们提出了准最大似然估计器(QMLE)和基于发散的估计器,其中最小密度功率发散估计器(MDPDE)用于稳健估计。为了评估这些估计器的性能,我们进行了蒙特卡罗模拟,并证明了 MDPDE 在零一膨胀模型中的充分性。我们还利用美国每月地震案例的数量进行了真实数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multiple values-inflated bivariate INAR time series of counts: featuring zero–one inflated Poisson-Lindly case

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Multiple values-inflated bivariate INAR time series of counts: featuring zero–one inflated Poisson-Lindly case

This study considers multiple values-inflated bivariate integer-valued autoregressive (MV-inflated BINAR) models. It develops the inferential procedures for parameter estimation on this model, which apply to constructing a change point test and outlier detection rule. We first introduce the MV-inflated BINAR model with one parameter exponential family and Poisson-Lindley innovations. Then, we propose a quasi-maximum likelihood estimator (QMLE) and divergence-based estimator featuring minimum density power divergence estimator (MDPDE) for robust estimation. To evaluate the performance of these estimators, we conduct Monte Carlo simulations and demonstrate the adequacy of MDPDE in zero–one inflated models. Real data analysis is also carried out using the number of monthly earthquake cases in the United States.

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