Iterated importance sampling in missing data problems

Computational Statistics and Data Analysis

Volumn 50, Issue 12, 2006, Pages 3386-3404

  Celeux, Gilles ^a   Marin, Jean Michel ^a,b   Robert, Christian P ^b,c  

^a INRIA   (France)

^b UNIVERSITÉ PARIS DAUPHINE   (France)

^c CREST   (France)

Author keywords

Adaptive algorithms; Bayesian inference; Latent variable models; Population Monte Carlo; Rao Blackwellisation; Stochastic volatility model

Indexed keywords

ADAPTIVE ALGORITHMS; ITERATIVE METHODS; MATHEMATICAL MODELS; PROBLEM SOLVING;

BAYESIAN INFERENCE; LATENT VARIABLE MODELS; POPULATION MONTE CARLO; RAO-BLACKWELLISATION; STOCHASTIC VOLATILITY MODEL;

DATA REDUCTION;

EID: 33646936073     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2005.07.018     Document Type: Article

References (35)

1. Andrieu, C., Robert, C., 2001. Controlled MCMC for optimal sampling. Technical Report 2001-25, Cahiers du CEREMADE, University Paris Dauphine.
2. Bollerslev T., Engle R., and Nelson D. ARCH models. In: Engle R.F., and MacFadden D. (Eds). The Handbook of Econometrics (1994), Elsevier Sciences, North-Holland Series in Statistics and Probability, Amsterdam
3. Cappé O., Guillin A., Marin J., and Robert C. Population Monte Carlo. J. Comput. Graphical Statist. 13 4 (2004) 907-929
4. Cappé O., Moulines E., and Rydén T. Inference in Hidden Markov Models (2005), Springer, New York
5. Casella G., and Robert C. Rao-Blackwellisation of sampling schemes. Biometrika 83 1 (1996) 81-94
6. Celeux G., Hurn M., and Robert C. Computational and inferential difficulties with mixture posterior distributions. J. Amer. Statist. Assoc. 95 (2000) 957-970
7. Chib S., Nadari F., and Shephard N. Markov chain Monte Carlo methods for stochastic volatility models. J. Econom. 108 (2002) 281-316
8. del Moral, P., Doucet, A., Peters, G., 2002. Sequential Monte Carlo samplers. Technical report, Department of Electrical Engineering, Cambridge University.
9. Dempster A.P., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. Ser. B 39 (1977) 1-38
10. Douc, R., Arnaud, G., Marin, J.-M., Robert, C., 2005. Convergence of adaptive sampling schemes. Technical Report 2005-6, Cahiers du CEREMADE, University Paris Dauphine.
11. Edmond, M., Raftery, A., Russell, J., 2001. Easy computation of Bayes factors and normalizing constants for mixture models via mixture importance sampling. Technical Report 398, Department of Statistics, University of Washington, Seattle.
12. Engle R. Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica 50 (1982) 987-1007
13. Everitt B. An Introduction to Latent Variable Models (1984), Chapman & Hall, London
14. Gatu C., and Kontoghiorghes E. Parallel algorithms for computing all possible subset regression models using the QR decomposition. Parallel Comput. 29 4 (2003) 505-521
15. Gelfand A., and Smith A. Sampling based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85 (1990) 398-409
16. Geweke J. Comment on Bayesian analysis of stochastic volatility models. J. Business Econom. Statist. 12 (1994) 397-399
17. Ghysels E., Harvey A., and Renault E. Stochastic volatility. In: Rao C., and Maddala G. (Eds). Statistical Methods in Finance (1996), Elsevier Science, North-Holland Series in Statistics and Probability, Amsterdam
18. Gilks W., and Wild P. Adaptative rejection sampling for Gibbs sampling. Appl. Statist. 41 (1992) 337-348
19. Haario H., Saksman E., and Tamminen J. Adaptive proposal distribution for random walk Metropolis algorithm. Comput. Statist. 14 (1999) 375-395
20. Haario H., Saksman E., and Tamminen J. An adaptive Metropolis algorithm. Bernoulli 7 2 (2001) 223-242
21. Jacquier R., Polson N., and Rossi P. Bayesian analysis of stochastic volatility models (with discussion). J. Business Econom. Statist. 12 (1994) 371-417
22. Jordan, M.I., 2004. Graphical models. Statist. Sci. 19, 140-155.
23. Kim S., Shephard N., and Chib S. Stochastic volatility: likelihood inference and comparison with ARCH models. Rev. Econom. Stud. 65 (1998) 361-393
24. Kontoghiorghes E. Parallel Algorithms for Linear Models: Numerical Methods and Estimation Problems. Advances in Computational Economics vol. 15 (2000), Kluwer Academic Publishers, Boston, MA
25. Little R., and Rubin D. Statistical Analysis with Missing Data (1987), Wiley, New York
26. Liu J., Wong W., and Kong A. Covariance structure of the Gibbs sampler with applications to the comparison of estimators and augmentation schemes. Biometrika 81 (1994) 27-40
27. McLachlan G.J., and Krishnan T. The EM Algorithm and Extensions (1997), Wiley, New York
28. McLachlan G., and Peel D. Finite Mixture Models (2000), Wiley, New York
29. Robert C., and Casella G. Monte Carlo Statistical Methods (1999), Springer, New York
30. Shephard N. Fitting non-linear time series models, with applications to stochastic variance models. J. Appl. Econom. 8 (1993) 135-152
31. Shephard N. Partial non-Gaussian state space analysis of non-Gaussian measurement times series. Biometrika 81 (1994) 115-131
32. Shephard N. Statistical aspects of ARCH and stochastic volatility. In: Cox D.R., Barndorff-Nielsen O.E., and Hinkley D.V. (Eds). Time Series Models in Econometrics, Finance and Other Fields (1996), Chapman & Hall
33. Shephard N., and Pitt M. Likelihood analysis of non-Gaussian measurement times series. Biometrika 84 (1997) 653-667
34. Tanner M., and Wong W. The calculation of posterior distribution distributions by data augmentation (with discussion). J. Amer. Statist. Assoc. 82 (1987) 528-550
35. Taylor S. Modelling stochastic volatility. Math. Finance 4 (1994) 183-204