Free Access
Issue |
ESAIM: PS
Volume 18, 2014
|
|
---|---|---|
Page(s) | 277 - 307 | |
DOI | https://doi.org/10.1051/ps/2013037 | |
Published online | 03 October 2014 |
- B.D.O. Anderson and M. Deistler, Identifiability in dynamic errors-in-variables models. J. Time Ser. Anal. 5 (1984) 1–13. [CrossRef] [Google Scholar]
- P. AngoNze, Critères d’ergodicité géométrique ou arithmétique de modèles linéaires perturbés à représentation markovienne. C. R. Acad. Sci. Paris Sér. I Math. 326 (1998) 371–376. [CrossRef] [MathSciNet] [Google Scholar]
- P.J. Bickel, Y. Ritov and T. Rydén, Asymptotic normality of the maximum–likelihood estimator for general hidden Markov models. Ann. Statist. 26 (1998) 1614–1635. [CrossRef] [MathSciNet] [Google Scholar]
- R.C. Bradley, Basic properties of strong mixing conditions, in Dependence in probability and statistics (Oberwolfach 1985). Boston, MA: Birkhäuser Boston, Progr. Probab. Statist. 11 (1986) 165–192. [Google Scholar]
- P.J. Brockwell and R.A. Davis, Time series: theory and methods (Second ed.). Springer Ser. Statistics. New York: Springer-Verlag (1991). [Google Scholar]
- C. Butucea and M.-L. Taupin, New M-estimators in semiparametric regression with errors in variables. Ann. Inst. Henri Poincaré, Probab. Stat. 44 (2008) 393–421. [CrossRef] [MathSciNet] [Google Scholar]
- K.C. Chanda, Large sample analysis of autoregressive moving-average models with errors in variables. J. Time Ser. Anal. 16 (1995) 1–15. [CrossRef] [Google Scholar]
- K.C. Chanda, Asymptotic properties of estimators for autoregressive models with errors in variables. Ann. Statist. 24 (1996) 423–430. [CrossRef] [MathSciNet] [Google Scholar]
- F. Comte and M.-L. Taupin, Semiparametric estimation in the (auto)-regressive β–mixing model with errors-in-variables. Math. Methods Statist. 10 (2001) 121–160. [MathSciNet] [Google Scholar]
- M. Costa and T. Alpuim, Parameter estimation of state space models for univariate observations. J. Statist. Plann. Inference 140 (2010) 1889–1902. [CrossRef] [MathSciNet] [Google Scholar]
- J. Dedecker F. Merlevède and M. Peligrad, A quenched weak invariance principle. Technical report, to appear in Ann. Inst. Henri Poincaré Probab. Statist. (2012). http://fr.arxiv.org/abs/math.ST/arxiv:1204.4554 [Google Scholar]
- J. Dedecker and C. Prieur, New dependence coefficients. Examples and applications to statistics. Probab. Theory Relat. Fields 132 (2005) 203–236. [CrossRef] [Google Scholar]
- J. Dedecker and E. Rio, On the functional central limit theorem for stationary processes. Ann. Inst. Henri Poincaré Probab. Statist. 36 (2000) 1–34. [Google Scholar]
- 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]
- R. Douc, E. Moulines, J. Olsson and R. van Handel, Consistency of the maximum likelihood estimator for general hidden markov models. Ann. Statist. 39 (2011) 474–513. [CrossRef] [MathSciNet] [Google Scholar]
- R. Douc, É. Moulines and T. Rydén, Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime. Ann. Statist. 32 (2004) 2254–2304. [CrossRef] [MathSciNet] [Google Scholar]
- C.-D. Fuh, Efficient likelihood estimation in state space models. Ann. Statist. 34 (2006) 2026–2068. [CrossRef] [MathSciNet] [Google Scholar]
- V. Genon−Catalot and C. Laredo, Leroux’s method for general hidden Markov models. Stochastic Process. Appl. 116 (2006) 222–243. [Google Scholar]
- E.J. Hannan, The asymptotic theory of linear time−series models. J. Appl. Probab. 10 (1973) 130–145. [CrossRef] [Google Scholar]
- J.L. Jensen and N.V. Petersen, Asymptotic normality of the maximum likelihood estimator in state space models. Ann. Statist. 27 (1999) 514–535. [CrossRef] [MathSciNet] [Google Scholar]
- B.G. Leroux, Maximum-likelihood estimation for hidden Markov models. Stochastic Process. Appl. 40 (1992) 127–143. [CrossRef] [MathSciNet] [Google Scholar]
- A. Mokkadem, Le modèle non linéaire AR(1) général. Ergodicité et ergodicité géométrique. C. R. Acad. Sci. Paris Sér. I Math. 301 (1985) 889–892. [Google Scholar]
- S. Na, S. Lee and H. Park, Sequential empirical process in autoregressive models with measurement errors. J. Statist. Plann. Inference 136 (2006) 4204–4216. [CrossRef] [MathSciNet] [Google Scholar]
- E. Nowak, Global identification of the dynamic shock-error model. J. Econom. 27 (1985) 211–219. [CrossRef] [Google Scholar]
- E. Rio, Covariance inequalities for strongly mixing processes. Ann. Inst. Henri Poincaré Probab. Statist. 29 (1993) 587–597. [Google Scholar]
- M. Rosenblatt, A central limit theorem and a strong mixing condition. Proc. Natl. Acad. Sci. USA 42 (1956) 43–47. [Google Scholar]
- J. Staudenmayer and J.P. Buonaccorsi, Measurement error in linear autoregressive models. J. Amer. Statist. Assoc. 100 (2005) 841–852. [CrossRef] [MathSciNet] [Google Scholar]
- A. Trapletti and K. Hornik, tseries: Time Series Analysis and Computational Finance. R package version 0.10-25 (2011). [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.