A Flexible Model for Time Series of Counts with Overdispersion or Underdispersion, Zero-Inflation and Heavy-Tailedness
Tóm tắt
Từ khóa
Tài liệu tham khảo
Al-Osh, M.A., Alzaid, A.A.: First order integer-valued autoregressive (INAR(1)) process. J. Time Ser. Anal. 8, 261–275 (1987)
Castillo, J., Pérez-Casany, M.: Overdispersed and underdispersed Poisson generalizations. J. Stat. Plan. Inference 134, 486–500 (2005)
Christiou, V., Fokians, K.: Quasi-likelihood inference for negative binomial time series models. J. Time Ser. Anal. 35, 55–78 (2014)
Davis, R., Liu, H.: Theory and inference for a class of nonlinear models with application to time series of counts. Stat. Sin. 26, 1673–1707 (2016)
Ferland, R., Latour, A., Oraichi, D.: Interger-valued GARCH process. J. Time Ser. Anal. 27, 923–942 (2006)
Fokianos, K., Rahbek, A., Tjstheim, D.: Poisson autoregression. J. Am. Stat. Assoc. 104, 1430–1439 (2009)
Gonçalves, E., Mendes-Lopes, N., Silva, F.: Infinitely divisible distributions in integer-valued GARCH models. J. Time. Ser. Anal. 36, 503–527 (2015)
Gorgi, P.: Beta-negative binomial auto-regressions for mdelling integer-valued time series with extreme observations. J. Roy. Stat. Soc. B 82, 1325–1347 (2020)
Guikema, S.D., Coffelt, J.P.: A flexible count data regression model for risk analysis. Risk. Anal. 28, 213–223 (2008)
Huang, J., Zhu, F.: An alternative test for zero modification in the INAR(1) model with Poisson innovations. Commun. Stati. Simul. Comput. Forthcom. (2022). https://doi.org/10.1080/03610918.2020.1869987
Imoto, T.: A generalized Conway-Maxwell-Poisson distribution which includes the negative binomial distribution. Appl. Math. Comput. 247, 824–834 (2014)
Jazi, M.A., Jones, G., Lai, C.D.: First-order integer valued AR processes with zero inflated poisson innovations. J. Time Ser. Anal. 33, 954–963 (2005)
Piancastelli, L.S.C., Barreto-Souza, W.: Inferential aspects of the zero-inflated Poisson INAR(1) process. Appl. Math. Model. 74, 457–468 (2019)
Qian, L., Li, Q., Zhu, F.: Modelling heavy-tailedness in count time series. Appl. Math. Model. 82, 766–784 (2020)
Rao, C.R.: On discrete distribution arising out of methods of ascertainment. Sankhyā. Series A 27, 311–324 (1965)
Shmueli, G., Minka, T.P., Kadane, J.B., Borle, S., Boatwright, P.: A useful distribution for fitting discrete data: Revival of the Conway-Maxwell-Poisson distribution. J. R. Stat. Soc. Ser. C 54, 127–42 (2005)
Silva, M.E., Pereira, I., McCabe, B.: Bayesian outlier detection in non-Gaussian autoregressive time series. J. Time. Ser. Anal. 40, 631–648 (2019)
Silva, R.B., Barreto-Souza, W.: Flexible and robust mixed Poisson INGARCH models. J. Time. Ser. Anal. 40, 788–814 (2019)
Steutel, F.W., varn Harn, K.: Discrete analogues of self-decomposability and stability. Ann. Probab. 7, 893–899 (1979)
Weiß, C.H.: Modelling time series of counts with overdispersion. Stat. Method. Appl. 18, 507–519 (2009)
Weiß, C.H., Homburg, H., Puig, P.: Testing for zero inflation and overdispersion in INAR(1) models. Stat. Pap. 60, 473–498 (2019)
Yang, M., Cavanaugh, J.E., Zamba, G.K.D.: State-space models for count time series with excess zeros. Stat. Model. 15, 70–90 (2015)
Yang, M., Zamba, G.K.D., Cavanaugh, J.E.: Markov regression models for count time series with excess zeros: A partial likelihood approach. Stat. Methodol. 14, 26–38 (2013)
Zhu, F.: Zero-inflated Poisson and negative binomial integer-valued GARCH models. J. Stat. Plan. Inference. 142, 826–839 (2012)
Zhu, F.: Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models. J. Math. Anal. Appl. 389, 58–71 (2012)
Zhu, F.: Modeling time series of counts with COM-Poisson INGARCH models. Math. Comput. Model. 56, 191–203 (2012)
Zhu, F., Liu, S., Shi, L.: Local influence analysis for Poisson autoregression with an application to stock transaction data. Stat. Neerl. 70, 4–25 (2016)
Zhu, F., Shi, L., Liu, S.: Influence diagnostics in log-linear integer-valued GARCH models. AStA Adv. Stat. Anal. 99, 311–335 (2015)
Zhu, R., Joe, H.: Modelling heavy-tailed count data using a generalised Poisson-inverse Gaussian family. Statist. Probab. Lett. 79, 1695–1703 (2009)
