观测驱动的新型时变系数泊松差分模型

Pub Date : 2024-08-07 DOI:10.1002/sta4.721
Ye Liu, Dehui Wang
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引用次数: 0

摘要

本文研究了一种由观测驱动的新型时变系数整数值时间序列模型。该模型基于泊松差分分布和扩展二叉稀疏算子,适用于负整数值时间序列建模。用于估计参数的主要方法是条件最小二乘法(CLS)和条件极大似然法(CML)。本文还讨论了估计结果的一致性和渐近正态性。本文采用似然比检验来检验协变量和观测值的存在性。通过数值模拟来验证模型的准确性和稳定性。最后,介绍了一个真实数据应用,以证明这一新提出模型的实用性和适应性。
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A novel time‐varying coefficient Poisson difference model driven by observation
This paper studies a novel time‐varying coefficient integer‐valued time series model driven by observation. The model is suitable for modeling negative integer‐valued time series based on the Poisson difference distribution and extended binomial thinning operator. Main methods used to estimate the parameters are the conditional least squares (CLS) and conditional maximum likelihood (CML) methods. This paper also discusses the consistency and asymptotic normality of the estimation results. Likelihood ratio tests are employed to examine the existence of covariate and observation. Numerical simulations are conducted to verify the accuracy and stability of the model. Finally, a real data application is presented to demonstrate the usefulness and adaptability of this newly proposed model.
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