Model selection for time series of count data

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

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.
Original languageEnglish
JournalComputational Statistics & Data Analysis
Publication statusPublished - 1 Jun 2018
Externally publishedYes


Dive into the research topics of 'Model selection for time series of count data'. Together they form a unique fingerprint.

Cite this