A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic Markov process
ESAIM: Probability and Statistics, Tome 18 (2014), pp. 726-749.

In this paper, we investigate a nonparametric approach to provide a recursive estimator of the transition density of a piecewise-deterministic Markov process, from only one observation of the path within a long time. In this framework, we do not observe a Markov chain with transition kernel of interest. Fortunately, one may write the transition density of interest as the ratio of the invariant distributions of two embedded chains of the process. Our method consists in estimating these invariant measures. We state a result of consistency and a central limit theorem under some general assumptions about the main features of the process. A simulation study illustrates the well asymptotic behavior of our estimator.

DOI : 10.1051/ps/2013054
Classification : 62G05, 62M05
Mots-clés : piecewise-deterministic Markov processes, nonparametric estimation, recursive estimator, transition kernel, asymptotic consistency
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     title = {A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic {Markov} process},
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Azaïs, Romain. A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic Markov process. ESAIM: Probability and Statistics, Tome 18 (2014), pp. 726-749. doi : 10.1051/ps/2013054. http://archive.numdam.org/articles/10.1051/ps/2013054/

[1] T. Aven and U. Jensen, Stochastic models in reliability, vol. 41 of Applications of Mathematics. Springer-Verlag, New York (1999). | MR | Zbl

[2] R. Azaïs, F. Dufour and A. Gégout-Petit, Nonparametric estimation of the conditional density of the inter-jumping times for piecewise-deterministic Markov processes. Preprint arXiv:1202.2212v2 (2012).

[3] A.K. Basu and D.K. Sahoo, On Berry-Esseen theorem for nonparametric density estimation in Markov sequences. Bull. Inform. Cybernet. 30 (1998) 25-39. | MR | Zbl

[4] D. Chafaï, F. Malrieu and K. Paroux, On the long time behavior of the TCP window size process. Stoch. Process. Appl. 120 (2010) 1518-1534. | MR | Zbl

[5] J. Chiquet and N. Limnios, A method to compute the transition function of a piecewise deterministic Markov process with application to reliability. Statist. Probab. Lett. 78 (2008) 1397-1403. | MR | Zbl

[6] S.J.M. Clémençon, Adaptive estimation of the transition density of a regular Markov chain. Math. Methods Statist. 9 (2000) 323-357. | MR | Zbl

[7] M.H.A. Davis, Markov models and optimization, vol. 49 of Monogr. Statist. Appl. Probab. Chapman & Hall, London (1993). | MR | Zbl

[8] P. Doukhan and M. Ghindès, Estimation de la transition de probabilité d'une chaîne de Markov Doëblin-récurrente. Étude du cas du processus autorégressif général d'ordre 1. Stoch. Process. Appl. 15 (1983) 271-293. | MR | Zbl

[9] M. Doumic, M. Hoffmann, N. Krell and L. Robert, Statistical estimation of a growth-fragmentation model observed on a genealogical tree. Preprint (2012).

[10] M. Duflo, Random iterative models. Appl. Math. Springer-Verlag, Berlin (1997). | MR | Zbl

[11] A. Genadot and M. Thieullen, Averaging for a fully coupled Piecewise Deterministic Markov Process in Infinite Dimensions. Adv. Appl. Probab. 44 3 (2012). | MR | Zbl

[12] O. Hernández-Lerma, S.O. Esparza and B.S. Duran, Recursive nonparametric estimation of nonstationary Markov processes. Bol. Soc. Mat. Mexicana 33 (1988) 57-69. | MR | Zbl

[13] O. Hernández-Lerma and J.B. Lasserre, Markov chains and invariant probabilities, vol. 211 of Progr. Math. Birkhäuser Verlag, Basel (2003). | MR | Zbl

[14] J. Hu, W. Wu and S. Sastry, Modeling subtilin production in bacillus subtilis using stochastic hybrid systems. Hybrid Systems: Computation and Control. Edited by R. Alur and G.J. Pappas. Lect. Notes Comput. Sci. Springer-Verlag, Berlin (2004). | Zbl

[15] C. Lacour, Adaptive estimation of the transition density of a Markov chain. Ann. Inst. Henri Poincaré, Probab. Statist. 43 (2007) 571-597. | Numdam | MR | Zbl

[16] C. Lacour, Nonparametric estimation of the stationary density and the transition density of a Markov chain. Stoch. Process. Appl. 118 (2008) 232-260. | MR | Zbl

[17] E. Liebscher, Density estimation for Markov chains. Statistics 23 (1992) 27-48. | MR | Zbl

[18] E. Masry and L. Györfi, Strong consistency and rates for recursive probability density estimators of stationary processes. J. Multivariate Anal. 22 (1987) 79-93. | MR | Zbl

[19] S. Meyn and R.L. Tweedie, Markov chains and stochastic stability, second edition. Cambridge University Press, Cambridge (2009). | MR | Zbl

[20] M. Rosenblatt, Density estimates and Markov sequences. In Nonparametric Techniques in Statistical Inference. Proc. of Sympos., Indiana Univ., Bloomington, Ind., 1969. Cambridge Univ. Press, London (1970), 199-213. | MR

[21] G.G. Roussas, Nonparametric estimation in Markov processes. Ann. Inst. Statist. Math. 21 (1969) 73-87. | MR | Zbl

[22] J. Van Ryzin, On strong consistency of density estimates. Ann. Math. Statist. 40 (1969) 1765-1772. | MR | Zbl

[23] S. Yakowitz, Nonparametric density and regression estimation for Markov sequences without mixing assumptions. J. Multivariate Anal. 30 (1989) 124-136. | MR | Zbl

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