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All issues Volume 20 (2016) ESAIM: PS, 20 (2016) 196-216 References
Free Access
Issue
ESAIM: PS
Volume 20, 2016
Page(s) 196 - 216
DOI https://doi.org/10.1051/ps/2016013
Published online 14 July 2016
  1. O.O. Aalen, Statistical inference for a family of counting processes. ProQuest LLC, Ann Arbor, MI. Ph.D. thesis, University of California, Berkeley (1975). [Google Scholar]
  2. O.O. Aalen, Weak convergence of stochastic integrals related to counting processes. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 38 (1977) 261–277. [CrossRef] [MathSciNet] [Google Scholar]
  3. O.O. Aalen, Nonparametric inference for a family of counting processes. Ann. Statist. 6 (1978) 701–726. [CrossRef] [MathSciNet] [Google Scholar]
  4. P.K. Andersen, Ø. Borgan, R.D. Gill and N. Keiding, Statistical models based on counting processes. Springer Series in Statistics. Springer-Verlag, New York (1993). [Google Scholar]
  5. S. Asmussen and H. Albrecher, Ruin probabilities. Vol. 14 of Adv. Ser. Statist. Sci. Appl. Probab. World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, 2nd edition (2010). [Google Scholar]
  6. R. Azaïs, F. Dufour and A. Gégout-Petit, Nonparametric estimation of the jump rate for non-homogeneous marked renewal processes. Ann. Inst. Henri Poincaré, Probab. Stat. 49 (2013) 1204–1231. [CrossRef] [MathSciNet] [Google Scholar]
  7. R. Azaïs, A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic Markov process. ESAIM: PS 18 (2014) 726–749. [CrossRef] [EDP Sciences] [Google Scholar]
  8. R. Azaïs, J.B. Bardet, A. Genadot, N. Krell and P.-A. Zitt, Piecewise deterministic Markov process (pdmps). Recent results. ESAIM: Proc. Survey 44 (2014) 276–290 [CrossRef] [EDP Sciences] [Google Scholar]
  9. R. Azaïs, F. Dufour and A. Gégout-Petit, Nonparametric estimation of the conditional distribution of the inter-jumping times for piecewise-deterministic Markov processes. Scandinavian J. Statist. 41 (2014) 950–969. [CrossRef] [Google Scholar]
  10. R. Azaïs and A. Genadot, Semi-parametric inference for the absorption features of a growth-fragmentation model. TEST 24 (2015) 341–360. [CrossRef] [MathSciNet] [Google Scholar]
  11. J.-B. Bardet, A. Christen and A. Guillin, F. Malrieu and P.-A. Zitt, Total variation estimates for the TCP process. Electron. J. Probab. 18 (2013) 10–21. [MathSciNet] [Google Scholar]
  12. P.H. Baxendale, Renewal theory and computable convergence rates for geometrically ergodic Markov chains. Ann. Appl. Probab. 15 (2005) 700–738. [CrossRef] [MathSciNet] [Google Scholar]
  13. P. Bertail, S. Clémençon and J. Tressou, A storage model with random release rate for modeling exposure to food contaminants. Math. Biosci. Eng. 5 (2008) 35–60. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  14. P. Bertail, S. Clémençon and J. Tressou, Statistical analysis of a dynamic model for dietary contaminant exposure. J. Biol. Dyn. 4 (2010) 212–234. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  15. F. Bouguet, Quantitative speeds of convergence for exposure to food contaminants. ESAIM: PS 19 (2015) 482–501. [CrossRef] [EDP Sciences] [Google Scholar]
  16. A.W. Bowman, An alternative method of cross-validation for the smoothing of density estimates. Biometrika 71 (1984) 353–360. [CrossRef] [MathSciNet] [Google Scholar]
  17. D. Chafaï, F. Malrieu and K. Paroux, On the long time behavior of the TCP window size process. Stoch. Process. Appl. 20 (2010) 1518–1534. [CrossRef] [MathSciNet] [Google Scholar]
  18. B. Cloez, Wasserstein decay of one dimensional jump-diffussions. Preprint HAL-00666720, arXiv:1202.1259 (2012). [Google Scholar]
  19. M.H.A. Davis, Markov models and optimization, Vol. 49 of Monographs on Statistics and Applied Probability. Chapman & Hall, London (1993). [Google Scholar]
  20. M.H.A. Davis, Piecewise-deterministic Markov processes: a general class of nondiffusion stochastic models. J. Roy. Statist. Soc. Ser. B 46 (1984) 353–388. With discussion. [MathSciNet] [Google Scholar]
  21. M. Doumic, M. Hoffmann, N. Krell and L. Robert, Statistical estimation of a growth-fragmentation model observed on a genealogical tree. Bernoulli 21 (2015) 1760–1799. [CrossRef] [MathSciNet] [Google Scholar]
  22. V. Dumas, F. Guillemin and Ph. Robert, A Markovian analysis of additive-increase multiplicative-decrease algorithms. Adv. Appl. Probab. 34 (2002) 85–111. [CrossRef] [MathSciNet] [Google Scholar]
  23. I. Grigorescu and M. Kang, Recurence and ergodicity for a continuous AIMD model. Preprint, available at http://www.math.miami.edu/˜igrigore/pp/b˙alpha˙0.pdf (2009). [Google Scholar]
  24. F. Guillemin, P. Robert and B. Zwart, AIMD algorithms and exponential functionals. Ann. Appl. Probab. 14 (2004) 90–117. [CrossRef] [MathSciNet] [Google Scholar]
  25. M. Hairer, Martin and J.F. Mattingly, Yet another look at Harris’ ergodic theorem for Markov chains. Seminar on Stochastic Analysis, Random Fields and Applications VI. Progr. Probab. 63 (2011) 109–117. [Google Scholar]
  26. P. Hall, J.S. Marron and B.U. Park, Smoothed cross-validation. Probab. Theory Related Fields 92 (1992) 1–20. [CrossRef] [MathSciNet] [Google Scholar]
  27. P. Laurenot and B. Perthame, Exponential decay for the growth-fragmentation/cell-division equation. Commun. Math. Sci. 7 (2009) 503–510. [CrossRef] [MathSciNet] [Google Scholar]
  28. M. Rudemo, Empirical choice of histograms and kernel density estimators. Scand. J. Statist. 9 (1982) 65–78. [MathSciNet] [Google Scholar]

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