Observation-driven models for Poisson counts
Observation-driven models for Poisson counts
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DOI：
10.1093/biomet/90.4.777
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发表时间：
2003-12
期刊：
影响因子：
2.7
通讯作者：
R. Davis;W. Dunsmuir;S. Streett
中科院分区：
文献类型：
--
作者：
R. Davis;W. Dunsmuir;S. Streett
本文讨论了一类一般的观测驱动的计数时间序列模型，其条件分布服从泊松分布。这些模型为对广泛的依赖结构建模提供了灵活的框架。建立了这些过程的平稳性和遍历性条件，由此可以得到极大似然估计量的大样本性质。提供了模拟以提供对估计器的有限样本行为的更多洞察。最后描述了悉尼一家医院哮喘病例日计数回归模型的应用。《比普里斯卡信托基金会2003》版权所有，牛津大学出版社。
This paper is concerned with a general class of observation-driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large-sample properties of the maximum likelihood estimators can be derived. Simulations are provided to give additional insight into the finite-sample behaviour of the estimators. Finally an application to a regression model for daily counts of asthma presentations at a Sydney hospital is described. Copyright Biometrika Trust 2003, Oxford University Press.