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Model selection for time series of count data

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E-pub ahead of print
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<mark>Journal publication date</mark>11/01/2018
<mark>Journal</mark>Computational Statistics and Data Analysis
<mark>State</mark>E-pub ahead of print
Early online date11/01/18
<mark>Original language</mark>English

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 modeling 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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in
Computational Statistics /