357
Views
17
CrossRef citations to date
0
Altmetric
Articles

Modelling interventions in INGARCH processes

, , &
Pages 640-657 | Received 30 Sep 2013, Accepted 21 Jul 2014, Published online: 27 Aug 2014

References

  • B. Abraham and G.E.P. Box, Bayesian analysis of some outlier problems in time series, Biometrika 66 (1979), pp. 229–236. doi: 10.1093/biomet/66.2.229
  • G.E.P. Box and G.C. Tiao, Intervention analysis with applications to economic and environmental problems, J. Amer. Statist. Assoc. 70 (1975), pp. 70–79. doi: 10.1080/01621459.1975.10480264
  • D.R. Cox, Statistical analysis of time series: Some recent developments, Scand. J. Statist. 8 (1981), pp. 93–115.
  • L. Fahrmeir and G. Tutz, Multivariate Statistical Modelling Based on Generalized Linear Models, Springer, New York, 2001.
  • R. Ferland, A. Latour, and D. Oraichi, Integer-valued GARCH process, J. Time Ser. Anal. 27 (2006), pp. 923–942. doi: 10.1111/j.1467-9892.2006.00496.x
  • K. Fokianos, Count time series models, in Time Series – Methods and Applications, T. Subba Rao, S. Subba Rao, and C. Rao, eds., Handbook of Statistics, no. 30, Elsevier, Amsterdam, 2012, pp. 315–347.
  • K. Fokianos and R. Fried, Interventions in INGARCH processes, J. Time Ser. Anal. 31 (2010), pp. 210–225. doi: 10.1111/j.1467-9892.2010.00657.x
  • K. Fokianos and R. Fried, Interventions in log-linear Poisson autoregression, Statist. Model. 12 (2012), pp. 299–322. doi: 10.1177/1471082X1201200401
  • K. Fokianos, A. Rahbek, and D. Tjøstheim, Poisson autoregression, J. Amer. Statist. Assoc. 104 (2009), pp. 1430–1439. doi: 10.1198/jasa.2009.tm08270
  • R. Fried, T. Liboschik, H. Elsaied, S. Kitromilidou, and K. Fokianos, On outliers and interventions in count time series following GLMs, Austrian J. Statist. 43 (2014), pp. 181–193.
  • A. Heinen, Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model, CORE discussion paper 62, 2003. Available at http://mpra.ub.uni-muenchen.de/8113/1/MPRA_paper_8113.pdf.
  • B. Kedem and K. Fokianos, Regression Models for Time Series Analysis, Wiley-Interscience, Hoboken, 2002.
  • K. Lange, Numerical Analysis for Statisticians, Springer, New York, 1999.
  • J. Manitz and M. Höhle, Bayesian outbreak detection algorithm for monitoring reported cases of campylobacteriosis in Germany, Biomet. J. 55 (2013), pp. 509–526. doi: 10.1002/bimj.201200141
  • R Core Team, R: A language and environment for statistical computing, 2013. Available from http://www.r-project.org.
  • W.N. Venables and B.D. Ripley, Modern Applied Statistics with S-PLUS, 3rd ed., Springer, New York, 1999.
  • C.H. Weiß, Modelling time series of counts with overdispersion, Statist. Meth. Appl. 18 (2009), pp. 507–519. doi: 10.1007/s10260-008-0108-6

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.