A Study on Mixture Poisson Autoregressive (P) Model
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Page Numbers:
382-394
Digital Object Identifier:
10.58578/mjms.v3i2.5379
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Authors:Yaska Mutah1*, S. S. Abdulkadir2, A. A. Akinrefon3, E. Torsen4
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
This research presents and assesses novel models for time series count data called the Mixture Poisson Autoregressive (MPAR) model addresses difficulties of discreteness, overdispersion, and serial correlation. A completely parametric technique was used, and a marginal distribution for the counts was defined. The parameters of the model were estimated using the Expectation Maximization method, through extensive Monte Carlo simulations, the stability of the estimates of the MPAR was evaluated and the results clearly revealed that the model was stable as the estimated parameters were converging to the values of the true parameters as the sample gets larger. Also, the results from the simulations revealed that the MPAR model outperform other count data models thus, Poisson distribution, Poisson Autoregressive (PAR) and Poisson Exponentially Weighted Moving Average (PEWMA).
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References
Brandt P. T. and Williams J. T. (2001). A Linear Poisson Autoregressive Model: The Poisson AR(p) Model. Political Analysis 9(2), 164-184.
Brandt, P. T., & Williams, J. T. (2001). A linear Poisson autoregressive model: The Poisson AR (p) model. Political Analysis, 9(2), 164-184.
Davide, R. (2021). Mixture autoregressive models with applications to heteroskedastic time series. The University of Manchester (United Kingdom).
Ghojogh, B., Ghojogh, A., Crowley, M., & Karray, F. (2019). Fitting a mixture distribution to data: tutorial. arXiv preprint arXiv:1901.06708.
Grunwald, G. K., Raftery, A. E., & Guttorp, P. (1993). Time series of continuous proportions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 55(1), 103-116.
Hartigan, J. A., & Hartigan, P. M. (1985). The dip test of unimodality. The annals of Statistics, 70-84.
Wong, C. S., & W. K. Li. 2000. On a mixture autoregressive model. Journal of Royal Statistical Society B 62:95–115.
Wood, S., Rosen, O. and Kohn, R. (2011). Bayesian mixtures of autoregressive models, Journal of Computational and Graphical Statistics 20(1), 174–195.
Yang, T.; Gallagher, C.M.; McMahan, C.S. (2019). A robust regression methodology via M-estimation. Commun. Stat. Theory Methods, 48, 1092–1107.