https://orcid.org/0000-0002-9311-5979
References
- M. Al-Osh and A. Alzaid, First-order integer-valued autoregressive (INAR(1)) process: distributional and regression properties, Stat. Neerl. 42 (1988), pp. 53–61. doi: 10.1111/j.1467-9574.1988.tb01521.x
- C. Antoniak, Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems, Ann. Stat. 2 (1974), pp. 1152–1174. doi: 10.1214/aos/1176342871
- Available at: http://www.forecastingprinciples.com/index.php/crimedata.
- Available at http://ark.intel.com/content/www/us/en/ark/products/149091/intel-core-i7-8565u-processor-8m-cache-up-to-4-60-ghz.html.
- D. Blackwell and J. MacQueen, Ferguson distributions via Pólya urn schemes, Ann. Stat. 1 (1973), pp. 353–355. doi: 10.1214/aos/1176342372
- R. Davis, S. Holan, R. Lund, and N. Ravishanker, Handbook of Discrete-valued Time Series, Chapman & Hall / CRC, New York, NY, 2015.
- R. Dorazio, On selecting a prior for the precision parameter of Dirichlet process mixture models, J. Stat. Plan. Inference 139 (2009), pp. 3384–3390. doi: 10.1016/j.jspi.2009.03.009
- D. Eddelbuettel, Seamless R and C++ Integration with Rcpp, Springer Publishing Company, Incorporated, New York, NY, 2013.
- M. Escobar and M. West, Computing nonparametric hierarchical models, in Practical nonparametric and semiparametric Bayesian statistics 1, D. Dey, P. Müller, and D. Sinha, eds., Springer-Verlag, 1998, pp. 1–22.
- T. Ferguson, A Bayesian analysis of some nonparametric problems, Ann. Stat. 1 (1973), pp. 209–230. doi: 10.1214/aos/1176342360
- K. Freeland, Statistical analysis of discrete time series with application to the analysis of workers' compensation data. PhD thesis, University of British Columbia, Vancouver, 1998.
- D. Gamerman and H. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Chapman & Hall / CRC, New York, NY, 2006.
- M. Jordan, Graphical models, Stat. Sci. 19 (2004), pp. 140–155. doi: 10.1214/088342304000000026
- E. McKenzie, Some simple models for discrete variate time series, J. Am. Water Res. Assoc. 21 (1985), pp. 645–650. doi: 10.1111/j.1752-1688.1985.tb05379.x
- R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2017.
- C. Robert and G. Casella, Monte Carlo Statistical Methods, Springer Science & Business Media, New York, NY, 2013.
- N. Silva, I. Pereira, and M.E. Silva, Forecasting in INAR(1) model, REVSTAT–Stat. J. 7 (2009), pp. 119–134.
- I. Silva, M.E. Silva, I. Pereira, and N. Silva, Replicated INAR(1) processes, Methodol. Comput. Appl. Probab. 7 (2005), pp. 517–542. doi: 10.1007/s11009-005-5006-x
- M. Tanner and W. Wong, The calculation of posterior distributions by data augmentation, J. Am. Stat. Assoc. 82 (1987), pp. 528–540. doi: 10.1080/01621459.1987.10478458
- D. Van Dyk and X.-L. Meng, The art of data augmentation, J. Comput. Graph. Stat. 10 (2001), pp. 1–50. doi: 10.1198/10618600152418584
- C.H. Weiß, Thinning operations for modeling time series of counts – a survey, AStA–Adv. Stat. Anal. 92 (2008), pp. 319. doi: 10.1007/s10182-008-0072-3
- C.H. Weiß, An Introduction to Discrete-valued Time Series, John Wiley & Sons, Hoboken, NJ, 2018.
- M. West, Hyperparameter estimation in Dirichlet process mixture models. Duke University ISDS discussion paper #92-A03, 1992.