Abstract
AbstractIn this paper, we present a novel family of multivariate mixed Poisson-Generalized Inverse Gaussian INAR(1), MMPGIG-INAR(1), regression models for modelling time series of overdispersed count response variables in a versatile manner. The statistical properties associated with the proposed family of models are discussed and we derive the joint distribution of innovations across all the sequences. Finally, for illustrative purposes different members of the MMPGIG-INAR(1) class are fitted to Local Government Property Insurance Fund data from the state of Wisconsin via maximum likelihood estimation.
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability
Reference50 articles.
1. Abdallah A, Boucher J-P, Cossette H (2016) Sarmanov family of multivariate distributions for bivariate dynamic claim counts model. Insurance 68:120–133
2. Al-Osh M, Alzaid AA (1987) First-order integer-valued autoregressive (INAR (1)) process. J Time Ser Anal 8(3):261–275
3. Amalia J, Purhadi, Otok BW (2017) Parameter estimation and statistical test of geographically weighted bivariate Poisson Inverse Gaussian regression models. In: AIP Conference Proceedings, vol 1905. AIP Publishing LLC, p 050005
4. Atkinson A, Yeh L (1982) Inference for Sichel’s compound Poisson distribution. J Am Stat Assoc 77(377):153–158
5. Barndorff-Nielsen O, Blaesild P, Seshadri V (1992) Multivariate distributions with generalized inverse Gaussian marginals, and associated Poisson mixtures. Can J Stat 109–120
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