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Research Article

Conway–Maxwell–Poisson seasonal autoregressive moving average model

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Pages 283-299 | Received 16 Mar 2021, Accepted 11 Jul 2021, Published online: 21 Jul 2021

References

  • Benjamin MA, Rigby RA, Stasinopoulos DM. Generalized autoregressive moving average models. J American Stat Assoc. 2003;98(461):214–223.
  • Talamantes J, Behseta S, Zender CS. Statistical modeling of valley fever data in Kern County, California. Int J Biometeorology. 2007;51(4):307–313.
  • Dugas AF, Jalalpour M, Gel Y, et al. Influenza forecasting with Google Flu Trends. PLoS One. 2013;8(2):e56176.
  • Albarracin OYE, Alencar AP, Lee Ho L. CUSUM chart to monitor autocorrelated counts using negative binomial GARMA model. Stat Meth Medical Res. 2018;27(9):2859–2871.
  • Albarracin OYE, Alencar AP, Lee Ho L. Effect of neglecting autocorrelation in regression EWMA charts for monitoring count time series. Quality Reliab Eng Int. 2018;34(8):1752–1762.
  • McCullagh P, Nelder J. Generalized linear models. 2nd ed. Chapman and Hall, London; 1989.
  • Rocha AV, Cribari-Neto F. Beta autoregressive moving average models. Test. 2009;18(3):529–545.
  • Bayer FM, Bayer DM, Pumi G. Kumaraswamy autoregressive moving average models for double bounded environmental data. J Hydrology. 2017;555:385–396.
  • Maior VQ, Cysneiros FJA. Symarma: a new dynamic model for temporal data on conditional symmetric distribution. Stat Papers. 2018;59(1):75–97.
  • Melo MS, Alencar AP. Conway-Maxwell-Poisson autoregressive moving average model for equidispersed, underdispersed, and overdispersed count data. J Time Ser Anal. 2020. DOI:https://doi.org/10.1111/jtsa.12550.
  • Briët OJ, Amerasinghe PH, Vounatsou P. Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PLoS One. 2013;8(6):e65761.
  • Box GE, Jenkins GM, Reinsel GC, et al. Time series analysis: forecasting and control. San Francisco: Holden-Day, John Wiley; 2015.
  • Tiku ML, Wong WK, Vaughan DC, et al. Time series models in non-normal situations: symmetric innovations. J Time Ser Anal. 2000;21(5):571–596.
  • Monteiro M, Scotto MG, Pereira I. Integer-valued autoregressive processes with periodic structure. J Stat Planning Inference. 2010;140(6):1529–1541.
  • Bourguignon M, LP Vasconcellos K, Reisen VA, et al. A Poisson INAR (1) process with a seasonal structure. J Stat Comput Simul. 2016;86(2):373–387.
  • Bayer FM, Cintra RJ, Cribari-Neto F. Beta seasonal autoregressive moving average models. J Stat Comput Simul. 2018;88(15):2961–2981.
  • Huang A. Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts. Stat Model. 2017;17(6):359–380.
  • Gay DM. Usage summary for selected optimization routines. Computing Science Technical Report. 1990;153:1–21.
  • R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. Available from: https://www.R-project.org/
  • Andersen EB. Asymptotic properties of conditional maximum-likelihood estimators. J R Stat Soc Ser B (Methodol). 1970;32:283–301.
  • Pumi G, Valk M, Bisognin C, et al. Beta autoregressive fractionally integrated moving average models. J Stat Planning Inference. 2019;200:196–212.
  • Pawitan Y. In all likelihood: statistical modelling and inference using likelihood. Oxford University Press, Oxford; 2001.
  • Kedem B, Fokianos K. Regression models for time series analysis. Vol. 488. Wiley, Hoboken; 2005.
  • Akaike H. A new look at the statistical model identification. IEEE Trans Automatic Control. 1974;19(6):716–723.
  • Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464.
  • Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graphical Stat. 1996;5(3):236–244.
  • Czado C, Gneiting T, Held L. Predictive model assessment for count data. Biometrics. 2009;65(4):1254–1261.
  • Jung RC, Tremayne A. Useful models for time series of counts or simply wrong ones?. AStA Adv Stat Anal. 2011;95(1):59–91.
  • Jung RC, McCabe BP, Tremayne AR. Model validation and diagnostics. In: Handbook of discrete-valued time series. Boca Raton (FL): Chapman & Hall/CRC; 2016. p. 189–218.
  • Evans D. Experimental evidence concerning contagious distributions in ecology. Biometrika. 1953;40(1/2):186–211.
  • Stasinopoulos MM, Rigby B. Package gamlss.util. page. 2016;9.
  • Ljung GM, Box GE. On a measure of lack of fit in time series models. Biometrika. 1978;65(2):297–303.

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