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Model selection for time series of count data

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  • Alzahrani, Naif
  • Neal, Peter
  • Spencer, Simon E.F.
  • McKinley, Trevelyan J.
  • Touloupou, Panayiota

Abstract

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.

Suggested Citation

  • Alzahrani, Naif & Neal, Peter & Spencer, Simon E.F. & McKinley, Trevelyan J. & Touloupou, Panayiota, 2018. "Model selection for time series of count data," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 33-44.
    • Handle: RePEc:eee:csdana:v:122:y:2018:i:c:p:33-44
    DOI: 10.1016/j.csda.2018.01.002
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    References listed on IDEAS

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    7. Kai Yang & Yao Kang & Dehui Wang & Han Li & Yajing Diao, 2019. "Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(7), pages 863-889, October.
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