Free Access
Issue
ESAIM: PS
Volume 19, 2015
Page(s) 268 - 292
DOI https://doi.org/10.1051/ps/2014024
Published online 06 October 2015
  1. P. Ailliot, Some theoretical results on Markov-switching autoregressive models with gamma innovations. C. R. Acad. Sci. Ser. I 343 (2006) 271–274. [CrossRef] [Google Scholar]
  2. P. Ailliot and V. Monbet, Markov-switching autoregressive models for wind time series. Environ. Model. Softw. 30 (2012) 92–101. [CrossRef] [Google Scholar]
  3. P. Ailliot, J. Bessac, V. Monbet, F. Pène, Non-homogeneous hidden Markov-switching models for wind time series. J. Stat. Plann. Inference 160 (2015) 75–88. [CrossRef] [Google Scholar]
  4. H.Z. An and F.C. Huang, The geometrical ergodicity of nonlinear autoregressive models. Stat. Sin. 6 (1996) 943–956. [Google Scholar]
  5. E. Bellone, J.P. Hughes and P. Guttorp, A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts. Climate Res. 15 (2000) 1–12. [CrossRef] [Google Scholar]
  6. O. Cappé, E. Moulines and T. Rydén, Inference in Hidden Markov Models. Springer-Verlag, New York (2005). [Google Scholar]
  7. R. Chen and R.S. Tsay, On the ergodicity of tar(1) processes. Ann. Appl. Probab. 1 (1991) 613–634. [CrossRef] [Google Scholar]
  8. F. Diebold, J-H Lee and G. Weinbach, Regime Switching with Time-Varying Transition Probabilities. Oxford University Press, Oxford (1994). [Google Scholar]
  9. R. Douc and C. Matias, Asymptotics of the maximum-likelihood estimator for general hidden Markov models. Bernoulli 7 (2001) 381–420. [CrossRef] [MathSciNet] [Google Scholar]
  10. R. Douc, E. Moulines and T. Rydn, Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime. Ann. Stat. 32 (2004) 2254–2304. [Google Scholar]
  11. J. Fan and Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods. Springer, New York (2003). [Google Scholar]
  12. C. Francq and M. Roussignol, Ergodicity of autoregressive processes with Markov-switching and consistency of the maximum-likelihood estimator. Statistics 32 (1998) 151–173. [CrossRef] [MathSciNet] [Google Scholar]
  13. J.D. Hamilton, A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57 (1989) 357–384. [CrossRef] [MathSciNet] [Google Scholar]
  14. J.P. Hughes, P. Guttorp and S.P. Charles, A non-homogeneous hidden Markov model for precipitation occurrence. J. R. Stat. Soc. Series C 48 (1999) 15–30. [CrossRef] [Google Scholar]
  15. C. Kim, J. Piger and R. Startz, Estimation of Markov regime-switching regression models with endogenous switching. J. Econ. 143 (2008) 263–273. [CrossRef] [Google Scholar]
  16. V. Krishnamurthy and T. Ryden, Consistent estimation of linear and non-linear autoregressive models with Markov regime. J. Time Ser. Anal. 19 (1998) 291–307. [CrossRef] [MathSciNet] [Google Scholar]
  17. H.M. Krolzig, Markov-switching vector autoregressions: modelling, statistical inference, and application to business cycle analysis. Lect. Notes Econ. Math. Systems 454 (1997). [Google Scholar]
  18. F. Le Gland and L. Mevel, Exponential forgetting and geometric ergodicity in hidden Markov models. Math. Control, Signals Syst. 13 (2000) 63–93. [Google Scholar]
  19. B. Leroux, Maximum-likelihood estimation for hidden Markov models. Stoch. Proc. Appl. 40 (1992) 127–143. [Google Scholar]
  20. T. Lindvall, Lectures on the Coupling Method. Corrected reprint of the 1992 original. Dover Publications, Inc., Mineola, NY (2002). [Google Scholar]
  21. S.P. Meyn, R.L Tweedie and P.W. Glynn, Markov Chains and Stochastic Stability. Cambridge, Cambridge University Press (2009), vol. 2. [Google Scholar]
  22. H. Teicher, Identifiability of finite mixtures. Ann. Math. Stat. 34 (1963) 1265–1269. [CrossRef] [Google Scholar]
  23. H. Tong, Non-Linear Time Series: A Dynamical System Approach. Oxford, UK, Oxford University Press (1990). [Google Scholar]
  24. I. Visser, M.E.J. Raijmakers and P. Molenaar, Confidence intervals for hidden Markov model parameters. British J. Math. Stat. Psychol. 53 (2000) 317–327. [CrossRef] [Google Scholar]
  25. M. Vrac and P. Naveau, Stochastic downscaling of precipitation: From dry events to heavy rainfalls. Water Res. Research 43 (2007) W07402. [CrossRef] [Google Scholar]
  26. J.F. Yao, On square-integrability of an AR process with Markov switching. Stat. Probab. Lett. 52 (2001) 265–270. [CrossRef] [Google Scholar]
  27. J.F. Yao and J.G. Attali, On stability of nonlinear AR processes with Markov switching. Adv. Appl. Probab. 32 (2000) 394–407. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.