Model selection for time series of count data
Alzahrani, N; Neal, P; Spencer, S; et al.McKinley, TJ; Touloupou, P
Date: 11 January 2018
Journal
Computational Statistics and Data Analysis
Publisher
Elsevier for International Association for Statistical Computing
Publisher DOI
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 observationdriven
integer valued autoregressive model when ...
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 observationdriven
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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0