Free Access
Issue |
ESAIM: PS
Volume 12, April 2008
|
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Page(s) | 196 - 218 | |
DOI | https://doi.org/10.1051/ps:2007045 | |
Published online | 23 January 2008 |
- Y. Aït-Sahalia, Maximum likelihood estimation of discretely sampled diffusions: a closed-form approximation approach. Econometrica 70 (2002) 223–262. [CrossRef] [MathSciNet] [Google Scholar]
- C. Andrieu and E. Moulines, On the ergodicity properties of some adaptive MCMC algorithms. Ann. Appl. Prob. 16 (2006) 1462–1505. [Google Scholar]
- 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]
- 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. [Google Scholar]
- S.L. Beal and L.B. Sheiner, Estimating population kinetics. Crit. Rev. Biomed. Eng. 8 (1982) 195–222. [Google Scholar]
- J.E. Bennet, A. Racine-Poon and J.C. Wakefield, MCMC for nonlinear hierarchical models. Chapman & Hall, London (1996) 339–358. [Google Scholar]
- 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]
- A. Beskos and G.O. Roberts, Exact simulation of diffusions. Ann. Appl. Prob. 15 (2005) 2422–2444. [Google Scholar]
- B.M. Bibby and M. Sørensen, Martingale estimation functions for discretely observed diffusion processes. Bernoulli 1 (1995) 17–39. [CrossRef] [MathSciNet] [Google Scholar]
- 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]
- 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]
- 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]
- B. Delyon, M. Lavielle and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm. Ann. Statist. 27 (1999) 94–128. [Google Scholar]
- 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]
- 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. [Google Scholar]
- S. Ditlevsen and A. De Gaetano, Mixed effects in stochastic differential equation models. REVSTAT- Statistical Journal 3 (2005) 137–153. [MathSciNet] [Google Scholar]
- S. Donnet and A. Samson, Estimation of parameters in incomplete data models defined by dynamical systems. J. Stat. Plan. Inf. (2007). [Google Scholar]
- 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]
- O. Elerian, S. Chib and N. Shephard, Likelihood inference for discretely observed nonlinear diffusions. Econometrica 69 (2001) 959–993. [CrossRef] [MathSciNet] [Google Scholar]
- B. Eraker, MCMC analysis of diffusion models with application to finance. J. Bus. Econ. Statist. 19 (2001) 177–191. [CrossRef] [Google Scholar]
- 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]
- A. Gloter and J. Jacod, Diffusions with measurement errors. I. Local asymptotic normality. ESAIM: PS 5 (2001) 225–242. [CrossRef] [EDP Sciences] [Google Scholar]
- A. Gloter and J. Jacod, Diffusions with measurement errors. II. Optimal estimators. ESAIM: PS 5 (2001) 243–260 (electronic). [CrossRef] [EDP Sciences] [Google Scholar]
- M. Kessler, Estimation of an ergodic diffusion from discrete observations. Scand. J. Statist. 24 (1997) 211–229. [CrossRef] [MathSciNet] [Google Scholar]
- R. Krishna, Applications of Pharmacokinetic principles in drug development. Kluwer Academic/Plenum Publishers, New York (2004). [Google Scholar]
- 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]
- E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models. Comput. Statist. Data Anal. 49 (2005) 1020–1038. [Google Scholar]
- 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]
- T. Kutoyants, Parameter estimation for stochastic processes. Helderman Verlag Berlin (1984). [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- R. Poulsen, Approximate maximum likelihood estimation of discretely observed diffusion process. Center for Analytical Finance, Working paper 29 (1999). [Google Scholar]
- B.L.S. Prakasa Rao, Statistical Inference for Diffusion Type Processes. Arnold Publisher (1999). [Google Scholar]
- 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]
- F. Schweppe, Evaluation of likelihood function for gaussian signals. IEEE Trans. Inf. Theory 11 (1965) 61–70. [CrossRef] [Google Scholar]
- 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]
- H. Sørensen, Parametric inference for diffusion processes observed at discrete points in time: a survey. Int. Stat. Rev 72 (2004) 337–354. [Google Scholar]
- M. Sørensen, Prediction-based estimating functions. Econom. J. 3 (2000) 123–147. [CrossRef] [MathSciNet] [Google Scholar]
- L. Tierney, Markov chains for exploring posterior distributions. Ann. Statist. 22 (1994) 1701–1762. [CrossRef] [MathSciNet] [Google Scholar]
- 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]
- 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]
- R. Wolfinger, Laplace's approximation for nonlinear mixed models. Biometrika 80 (1993) 791–795. [CrossRef] [MathSciNet] [Google Scholar]
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