Model selection for time series of count data
Model selection for time series of count data
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DOI：
10.1016/j.csda.2018.01.002
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
N. Alzahrani;P. Neal;S. Spencer;T. McKinley;Panayiota Touloupou
中科院分区：
文献类型：
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作者：
N. Alzahrani;P. Neal;S. Spencer;T. McKinley;Panayiota Touloupou
在竞争统计模型之间进行选择是一个具有挑战性的问题，特别是当竞争模型是非嵌套的时。一个有效的算法开发的贝叶斯框架之间的选择参数驱动的自回归泊松回归模型和观测驱动的整数值自回归模型建模时，时间序列计数数据。为了实现这一点的粒子MCMC算法的自回归泊松回归模型的介绍。支持粒子MCMC算法的粒子滤波器在通过重要性采样估计自回归泊松回归模型的边缘似然性中起着关键作用，并且还用于估计DIC。模型选择算法的性能进行评估，通过模拟研究。两个现实生活中的数据集，每月美国脊髓灰质炎病例（1970年至1983年）和每月的福利索赔伐木业的不列颠哥伦比亚省工人赔偿委员会（1985年至1994年）成功地进行了分析。
Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. An effective algorithm is developed in a Bayesian framework for selecting between a parameter-driven autoregressive Poisson regression model and an observation-driven integer valued autoregressive model when modelling time series count data. In order to achieve this a particle MCMC algorithm for the autoregressive Poisson regression model is introduced. The particle filter underpinning the particle MCMC algorithm plays a key role in estimating the marginal likelihood of the autoregressive Poisson regression model via importance sampling and is also utilised to estimate the DIC. The performance of the model selection algorithms are assessed via a simulation study. Two real-life data sets, monthly US polio cases (1970–1983) and monthly benefit claims from the logging industry to the British Columbia Workers Compensation Board (1985–1994) are successfully analysed.