Free Access
Issue
ESAIM: PS
Volume 12, April 2008
Page(s) 196 - 218
DOI https://doi.org/10.1051/ps:2007045
Published online 23 January 2008
  1. Y. Aït-Sahalia, Maximum likelihood estimation of discretely sampled diffusions: a closed-form approximation approach. Econometrica 70 (2002) 223–262. [CrossRef] [MathSciNet] [Google Scholar]
  2. C. Andrieu and E. Moulines, On the ergodicity properties of some adaptive MCMC algorithms. Ann. Appl. Prob. 16 (2006) 1462–1505. [CrossRef] [Google Scholar]
  3. V. Bally and D. Talay, The law of the Euler Scheme for Stochastic Differential Equations: I. Convergence Rate of the Density. Technical Report 2675, INRIA (1995). [Google Scholar]
  4. V. Bally and D. Talay, The law of the Euler scheme for stochastic differential equations (II): convergence rate of the density. Monte Carlo Methods Appl. 2 (1996) 93–128. [CrossRef] [MathSciNet] [Google Scholar]
  5. S.L. Beal and L.B. Sheiner, Estimating population kinetics. Crit. Rev. Biomed. Eng. 8 (1982) 195–222. [PubMed] [Google Scholar]
  6. J.E. Bennet, A. Racine-Poon and J.C. Wakefield, MCMC for nonlinear hierarchical models. Chapman & Hall, London (1996) 339–358. [Google Scholar]
  7. A. Beskos, O. Papaspiliopoulos, G.O. Roberts and P. Fearnhead, Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes. J. R. Stat. Soc. B 68 (2006) 333–382. [CrossRef] [Google Scholar]
  8. A. Beskos and G.O. Roberts, Exact simulation of diffusions. Ann. Appl. Prob. 15 (2005) 2422–2444. [CrossRef] [Google Scholar]
  9. B.M. Bibby and M. Sørensen, Martingale estimation functions for discretely observed diffusion processes. Bernoulli 1 (1995) 17–39. [CrossRef] [MathSciNet] [Google Scholar]
  10. G. Celeux and J. Diebolt, The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational. Statistics Quaterly 2 (1985) 73–82. [Google Scholar]
  11. D. Dacunha-Castelle and M. Duflo, Probabilités et statistiques. Tome 2. Collection Mathématiques Appliquées pour la Maîtrise. [Collection of Applied Mathematics for the Master's Degree]. Masson, Paris, 1983. Problèmes à temps mobile. [Movable-time problems]. [Google Scholar]
  12. D. Dacunha-Castelle and D. Florens-Zmirou, Estimation of the coefficients of a diffusion from discrete observations. Stochastics 19 (1986) 263–284. [MathSciNet] [Google Scholar]
  13. B. Delyon, M. Lavielle and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27 (1999) 94–128. [CrossRef] [MathSciNet] [Google Scholar]
  14. A. Dembo and O. Zeitouni, Parameter estimation of partially observed continuous time stochastic processes via the EM algorithm. Stoch. Process. Appl. 23 (1986) 91–113. [CrossRef] [Google Scholar]
  15. A P. Dempster, N.M. Laird and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39 (1977) 1–38. With discussion. [MathSciNet] [Google Scholar]
  16. S. Ditlevsen and A. De Gaetano, Mixed effects in stochastic differential equation models. REVSTAT- Statistical Journal 3 (2005) 137–153. [MathSciNet] [Google Scholar]
  17. S. Donnet and A. Samson, Estimation of parameters in incomplete data models defined by dynamical systems. J. Stat. Plan. Inf. (2007). [Google Scholar]
  18. 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]
  19. O. Elerian, S. Chib and N. Shephard, Likelihood inference for discretely observed nonlinear diffusions. Econometrica 69 (2001) 959–993. [CrossRef] [MathSciNet] [Google Scholar]
  20. B. Eraker, MCMC analysis of diffusion models with application to finance. J. Bus. Econ. Statist. 19 (2001) 177–191. [CrossRef] [Google Scholar]
  21. V. Genon-Catalot and J. Jacod, On the estimation of the diffusion coefficient for multi-dimensional diffusion processes. Ann. Inst. H. Poincaré Probab. Statist. 29 (1993) 119–151. [MathSciNet] [Google Scholar]
  22. A. Gloter and J. Jacod, Diffusions with measurement errors. I. Local asymptotic normality. ESAIM: PS 5 (2001) 225–242. [CrossRef] [EDP Sciences] [Google Scholar]
  23. A. Gloter and J. Jacod, Diffusions with measurement errors. II. Optimal estimators. ESAIM: PS 5 (2001) 243–260 (electronic). [CrossRef] [EDP Sciences] [Google Scholar]
  24. M. Kessler, Estimation of an ergodic diffusion from discrete observations. Scand. J. Statist. 24 (1997) 211–229. [CrossRef] [MathSciNet] [Google Scholar]
  25. R. Krishna, Applications of Pharmacokinetic principles in drug development. Kluwer Academic/Plenum Publishers, New York (2004). [Google Scholar]
  26. E. Kuhn and M. Lavielle, Coupling a stochastic approximation version of EM with a MCMC procedure. ESAIM: PS 8 (2004) 115–131. [CrossRef] [EDP Sciences] [Google Scholar]
  27. E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models. Comput. Statist. Data Anal. 49 (2005) 1020–1038. [CrossRef] [MathSciNet] [Google Scholar]
  28. S. Kusuoka and D. Stroock, Applications of the malliavin calculus, part II. J. Fac. Sci. Univ. Tokyo. Sect. IA, Math. 32 (1985) 1–76. [Google Scholar]
  29. T. Kutoyants, Parameter estimation for stochastic processes. Helderman Verlag Berlin (1984). [Google Scholar]
  30. M.L. Lindstrom and D.M. Bates, Nonlinear mixed effects models for repeated measures data. Biometrics 46 (1990) 673–687. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  31. T.A. Louis, Finding the observed information matrix when using the EM algorithm. J. Roy. Statist. Soc. Ser. B 44 (1982) 226–233. [MathSciNet] [Google Scholar]
  32. R.V. Overgaard, N. Jonsson, C.W. Tornøe and H. Madsen, Non-linear mixed-effects models with stochastic differential equations: Implementation of an estimation algorithm. J Pharmacokinet. Pharmacodyn. 32 (2005) 85–107. [CrossRef] [PubMed] [Google Scholar]
  33. A.R. Pedersen, A new approach to maximum likelihood estimation for stochastic differential equations based on discrete observations. Scand. J. Statist. 22 (1995) 55–71. [MathSciNet] [Google Scholar]
  34. J.C. Pinheiro and D.M. Bates, Approximations to the log-likelihood function in the non-linear mixed-effect models. J. Comput. Graph. Statist. 4 (1995) 12–35. [CrossRef] [Google Scholar]
  35. R. Poulsen, Approximate maximum likelihood estimation of discretely observed diffusion process. Center for Analytical Finance, Working paper 29 (1999). [Google Scholar]
  36. B.L.S. Prakasa Rao, Statistical Inference for Diffusion Type Processes. Arnold Publisher (1999). [Google Scholar]
  37. G.O. Roberts and O. Stramer, On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm. Biometrika 88 (2001) 603–621. [CrossRef] [MathSciNet] [Google Scholar]
  38. F. Schweppe, Evaluation of likelihood function for gaussian signals. IEEE Trans. Inf. Theory 11 (1965) 61–70. [CrossRef] [Google Scholar]
  39. H. Singer, Continuous-time dynamical systems with sampled data, error of measurement and unobserved components. J. Time Series Anal. 14 (1993) 527–545. [CrossRef] [Google Scholar]
  40. H. Sørensen, Parametric inference for diffusion processes observed at discrete points in time: a survey. Int. Stat. Rev 72 (2004) 337–354. [Google Scholar]
  41. M. Sørensen, Prediction-based estimating functions. Econom. J. 3 (2000) 123–147. [CrossRef] [MathSciNet] [Google Scholar]
  42. L. Tierney, Markov chains for exploring posterior distributions. Ann. Statist. 22 (1994) 1701–1762. [CrossRef] [MathSciNet] [Google Scholar]
  43. C.W. Tornøe, R.V. Overgaard, H. Agersø, H.A. Nielsen, H. Madsen and E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations. Pharm. Res. 22 (2005) 1247–1258. [CrossRef] [PubMed] [Google Scholar]
  44. G.C.G. Wei and M.A. Tanner, Calculating the content and boundary of the highest posterior density region via data augmentation. Biometrika 77 (1990) 649–652. [CrossRef] [MathSciNet] [Google Scholar]
  45. R. Wolfinger, Laplace's approximation for nonlinear mixed models. Biometrika 80 (1993) 791–795. [CrossRef] [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.