Free Access
Volume 12, April 2008
Page(s) 196 - 218
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]
  2. C. Andrieu and E. Moulines, On the ergodicity properties of some adaptive MCMC algorithms. Ann. Appl. Prob. 16 (2006) 1462–1505. [CrossRef]
  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).
  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]
  5. S.L. Beal and L.B. Sheiner, Estimating population kinetics. Crit. Rev. Biomed. Eng. 8 (1982) 195–222. [PubMed]
  6. J.E. Bennet, A. Racine-Poon and J.C. Wakefield, MCMC for nonlinear hierarchical models. Chapman & Hall, London (1996) 339–358.
  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]
  8. A. Beskos and G.O. Roberts, Exact simulation of diffusions. Ann. Appl. Prob. 15 (2005) 2422–2444. [CrossRef]
  9. B.M. Bibby and M. Sørensen, Martingale estimation functions for discretely observed diffusion processes. Bernoulli 1 (1995) 17–39. [CrossRef] [MathSciNet]
  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.
  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].
  12. D. Dacunha-Castelle and D. Florens-Zmirou, Estimation of the coefficients of a diffusion from discrete observations. Stochastics 19 (1986) 263–284. [MathSciNet]
  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]
  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]
  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]
  16. S. Ditlevsen and A. De Gaetano, Mixed effects in stochastic differential equation models. REVSTAT- Statistical Journal 3 (2005) 137–153. [MathSciNet]
  17. S. Donnet and A. Samson, Estimation of parameters in incomplete data models defined by dynamical systems. J. Stat. Plan. Inf. (2007).
  18. R. Douc and C. Matias, Asymptotics of the maximum likelihood estimator for general hidden Markov models. Bernoulli 7 (2001) 381–420. [CrossRef] [MathSciNet]
  19. O. Elerian, S. Chib and N. Shephard, Likelihood inference for discretely observed nonlinear diffusions. Econometrica 69 (2001) 959–993. [CrossRef] [MathSciNet]
  20. B. Eraker, MCMC analysis of diffusion models with application to finance. J. Bus. Econ. Statist. 19 (2001) 177–191. [CrossRef]
  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]
  22. A. Gloter and J. Jacod, Diffusions with measurement errors. I. Local asymptotic normality. ESAIM: PS 5 (2001) 225–242. [CrossRef] [EDP Sciences]
  23. A. Gloter and J. Jacod, Diffusions with measurement errors. II. Optimal estimators. ESAIM: PS 5 (2001) 243–260 (electronic). [CrossRef] [EDP Sciences]
  24. M. Kessler, Estimation of an ergodic diffusion from discrete observations. Scand. J. Statist. 24 (1997) 211–229. [CrossRef] [MathSciNet]
  25. R. Krishna, Applications of Pharmacokinetic principles in drug development. Kluwer Academic/Plenum Publishers, New York (2004).
  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]
  27. E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models. Comput. Statist. Data Anal. 49 (2005) 1020–1038. [CrossRef] [MathSciNet]
  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.
  29. T. Kutoyants, Parameter estimation for stochastic processes. Helderman Verlag Berlin (1984).
  30. M.L. Lindstrom and D.M. Bates, Nonlinear mixed effects models for repeated measures data. Biometrics 46 (1990) 673–687. [CrossRef] [MathSciNet] [PubMed]
  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]
  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]
  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]
  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]
  35. R. Poulsen, Approximate maximum likelihood estimation of discretely observed diffusion process. Center for Analytical Finance, Working paper 29 (1999).
  36. B.L.S. Prakasa Rao, Statistical Inference for Diffusion Type Processes. Arnold Publisher (1999).
  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]
  38. F. Schweppe, Evaluation of likelihood function for gaussian signals. IEEE Trans. Inf. Theory 11 (1965) 61–70. [CrossRef]
  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]
  40. H. Sørensen, Parametric inference for diffusion processes observed at discrete points in time: a survey. Int. Stat. Rev 72 (2004) 337–354.
  41. M. Sørensen, Prediction-based estimating functions. Econom. J. 3 (2000) 123–147. [CrossRef] [MathSciNet]
  42. L. Tierney, Markov chains for exploring posterior distributions. Ann. Statist. 22 (1994) 1701–1762. [CrossRef] [MathSciNet]
  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]
  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]
  45. R. Wolfinger, Laplace's approximation for nonlinear mixed models. Biometrika 80 (1993) 791–795. [CrossRef] [MathSciNet]

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