Free Access
Volume 20, 2016
Page(s) 196 - 216
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˜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]

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.