Parameter-driven state-space model for integer-valued time series with application. Issue 8 (24th May 2019)

• Record Type: Journal Article
• Authors: Koh, Y. B.; Bukhari, N. A.; Mohamed, I.
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 8(2019)
• Page Start: 1394
• Page End: 1409
• Publication Date: 2019-05-24
• Subjects: Parameter-driven -- dengue data -- integer-valued time series -- sequential monte carlo -- particle filter -- state-space models; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1582653
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Time series of counts occur in many different contexts, the counts being usually of certain events or objects in specified time intervals. In this paper we introduce a model called parameter-driven state-space model to analyse integer-valued time series data. A key property of such model is that the distribution of the observed count data is independent, conditional on the latent process, although the observations are correlated marginally. Our simulation shows that the Monte Carlo Expectation Maximization (MCEM) algorithm and the particle method are useful for the parameter estimation of the proposed model. In the application to Malaysia dengue data, our model fits better when compared with several other models including that of Yang et al. (2015)