Dynamic detection of change points in long time series

Annals of the Institute of Statistical Mathematics

Volumn 59, Issue 2, 2007, Pages 349-366

  Chopin, Nicolas ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Change point models; GARCH models; Markov chain Monte Carlo; Particle filter; Sequential Monte Carlo; State state models

Indexed keywords

BAYESIAN NETWORKS; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBLEM SOLVING; SEQUENTIAL MACHINES; STATE SPACE METHODS;

CHANGE POINT MODELS; GARCH MODELS; HYBRID ALGORITHMS; MARKOV CHAIN MONTE CARLO; PARTICLE FILTERS; SEQUENTIAL MONTE CARLO; STATE STATE MODELS;

DYNAMIC PROGRAMMING;

EID: 33847358600     PISSN: 00203157     EISSN: 15729052     Source Type: Journal    
DOI: 10.1007/s10463-006-0053-9     Document Type: Article

References (29)

1. Barry, D., Hartigan, J. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88, 309-319
2. Carter, C. K., Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541-553.
3. Casella, G., Robert, C. (1996). Rao-Blackwellisation of sampling schemes. Biometrika, 1, 81-94.
4. Chen, R., Liu, J. (2000). Mixture Kalman filters. Journal of the Royal Statistical Society Series B, 62, 493-508.
5. Chib, S. (1998). Estimation and comparison of multiple change-point models. Journal of Econometrics, 86, 221-241.
6. Chopin, N. (2002). A sequential particle filter for static models. Biometrika, 89, 539-552.
7. Chopin, N. (2004). Central Limit Theorem for sequential Monte Carlo methods and its application to Bayesian inference. Annals of Statistics, 32(6), 2385-2411.
8. Chopin, N., Pelgrin, F. (2004). Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation. Journal of Econometrics, 123(2), 327-344.
9. de Jong, P.. Shephard, N. (1995). The simulation smoother for time series models. Biometrika, 82, 339-350.
10. Del Moral, P., Miclo, L. (2000). Branching and interacting particle systems approximations of Feynman-Kac formulae with applications to non-linear filtering. In J. Azéma, M. Emery, M. Ledoux, M. Yor, (Eds.) Séminaire de Probabilités XXXIV, (vol 1729 pp 1-145). Lecture Notes in Mathematics, Berlin Heidelberg New York: Springer.
11. Doucet, A., de Freitas, N., Gordon, N. (2001). Sequential Monte Carlo Methods in Practice, Berlin Heidelberg New York: Springer.
12. Doucet, A., Godsill, S., Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208.
13. Früwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of Time Series Analysis, 15, 183-202.
14. Gerlach, R., Carter, C., Kohn, R. (2000). Efficient Bayesian inference for dynamic mixture models. Journal of the American Statistical Association, 88, 819-828.
15. Gilks, W. R. and Berzuini, C. (2001). Following a moving target - Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society Series B, 63, 127-146.
16. Godsill, S., Doucet, A., West, M. (2004). Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association, 99, 156-168.
17. Gordon, N. J., Salmond, D. J., Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceeding Communications, Radar, and Signal Processing, 140(2), 107-113.
18. Green, P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732.
19. Kalman, R., Bucy, R. (1961). New results in linear filtering and prediction theory. Transactions of the American Society of Mechanical Engineers, 83, 95-108.
20. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25.
21. Künsch, H. (2001). State space and hidden Markov models. In O. E. Barndorff-Nielsen, D. R. Cox, C. Klüppelberg (Eds.) Complex stochastic systems, (pp 109-173). London: Chapman and Hall.
22. Liu, J., Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1032-1044.
23. McCulloch, R., Tsay, R. (1993). Bayesian inference and prediction for mean and variance shifts in autoregressive time series. Journal of the American Statistical Association, 88, 968-978.
24. Mikosch, T., Stǎricǎ, C. (2003). Long range dependence effects and ARCH modelling. In P. Doukhan, G. Oppenheim, M. Taqqu (Eds.) Theory and applications of long range dependence. Boston: Birkhauser.
25. Mikosch, T., Stǎricǎ, C. (2004). Non-stationarities in financial time series, the long range dependence and the IGARCH effects. Review of Economics and Statistics, 86, 378-390.
26. Pitt, M., Shephard, N. (1999). Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association, 94, 590-599.
27. Robert, C. P., Casella, G. (2004). Monte Carlo statistical methods, 2nd edn. Berlin Heidelberg New York: Springer
28. Roberts, G., Gelman, A., Gilks, W. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms. Annals of Applied probability 7, 110-120.
29. Stephens, D. A. (1994). Bayesian retrospective multiple-changepoint identification. Applied Statistics, 43(1), 159-178.