Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome

Computational Statistics and Data Analysis

Volumn 53, Issue 9, 2009, Pages 3314-3323

  Tian, Guo Liang ^a   Ng, Kai Wang ^a   Li, Kai Can ^b   Tan, Ming ^c  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b HUBEI NORMAL UNIVERSITY   (China)

^c UNIVERSITY OF MARYLAND GREENEBAUM CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROPRIATE MODELS; BAYES FACTORS; BAYESIAN; BAYESIAN METHODS; BAYESIAN SAMPLINGS; BIRMINGHAM; CHANGE-POINT ANALYSIS; CHANGE-POINTS; CONDITIONAL DISTRIBUTIONS; ENGLAND; INVERSE BAYES FORMULAE; MARGINAL LIKELIHOODS; MARKOV CHAIN MONTE CARLO METHODS; NON-ITERATIVE; OBSERVED DATUM; POSTERIOR DISTRIBUTIONS; SAMPLING-BASED;

BAYESIAN NETWORKS; MARKOV PROCESSES;

MONTE CARLO METHODS;

EID: 64749090747     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.02.006     Document Type: Article

References (31)

1. Arnold S.F. Gibbs sampler. In: Rao C.R. (Ed). Handbook of Statistics vol. 9 (1993), Elsevier Science Publishers B. V. 599-625
2. Barry D., and Hartigan J.A. Product partition models for change point problems. Annals of Statistics 20 (1992) 260-279
3. Barry D., and Hartigan J.A. A Bayesian analysis for change point problems. Journal of the American Statistical Association 88 (1993) 309-319
4. Brodsky B.E., and Darkhovsky B.S. Nonparametric Methods in Change Point Problems. Mathematics and Its Applications vol. 243 (1993), Springer, New York
5. Carlin B.P., Gelfand A.E., and Smith A.F.M. Hierarchical Bayesian analysis of changepoint problems. Applied Statistics 41 (1992) 389-405
6. Chen M.H. Computing marginal likelihoods from a single MCMC output. Statistica Neerlandica 59 (2005) 16-29
7. Chen J., and Gupta A.K. Parametric Statistical Change Point Analysis (2000), Springer, New York
8. Chib S. Estimation and comparison of multiple change-point models. Journal of Econometrics 86 (1998) 221-241
9. Chib S. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90 (1995) 1313-1321
10. Evans M., and Swartz T. Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems (with discussions). Statistical Science 10 (1995) 254-272
11. Evans M., and Swartz T. Approximating Integrals via Monte Carlo and Deterministic Methods (2000), Oxford University Press, Oxford
12. Fearnhead P. Exact and efficient inference for multiple changepoint problems. Statistics and Computing 16 (2006) 203-213
13. Fearnhead P., and Liu Z. On-line inference for multiple changepoint problems. Journal of the Royal Statistical Society, Series B 64 (2007) 589-605
14. Gelfand A.E., and Dey D.K. Bayesian model choice: Asymptotics and exact calculations. Journal of the Royal Statistical Society, Series B 56 (1994) 501-514
15. Halpern A.L. Minimally selected p and other tests for a single abrupt changepoint in a binary sequence. Biometrics 55 (1999) 1044-1050
16. Henderson R., and Matthews J.N.S. An investigation of changepoints in the annual number of cases of haemolytic uraemic syndrome. Applied Statistics 42 (1993) 461-471
17. Hobert J.P., and Casella G. The effect of improper priors on Gibbs sampling in hierarchical linear models. Journal of the American Statistical Association 91 (1996) 1461-1473
18. Jarrett R.G. A note on the intervals between coal-mining disasters. Biometrika 66 (1979) 191-193
19. Jeffreys H. Theory of Probability. 3rd ed. (1961), Oxford University Press, Oxford
20. Kass R.E., and Raftery A.E. Bayes factors. Journal of the American Statistical Association 90 (1995) 773-795
21. Maguire B.A., Pearson E.S., and Wynn A.H.A. The time intervals between industrial accidents. Biometrika 38 (1952) 168-180
22. Milford D.V., Taylor C.M., Gutteridge B., Hall S.M., Rowe B., and Kleanthous H. Haemolytic uraemic syndromes in the British Isles 1985-8: Association with verocytotoxin producing Escherichia coli Part 1: Clinical and epidemiological aspects. Archieves for Diseases in Childhood 65 7 (1990) 716-721
23. Raftery A.E., and Akman V.E. Bayesian analysis of a Poisson process with a change-point. Biometrika 73 (1986) 85-89
24. Siegmund D. Confidence sets in change point problems. International Statistical Review 56 (1988) 31-48
25. Smith A.F.M. A Bayesian approach to inference about a change-point in a sequence of random variables. Biometrika 62 (1975) 407-416
26. Smith A.F.M. Change-point problems: Approaches and applications. In: Bernardo J.M., DeGroot M.H., Lindley D.V., and Smith A.F.M. (Eds). Bayesian Statistics, vol. 1 (1980), Valencia University Press, Valencia 83-89
27. Stephens D.A. Bayesian retrospective multiple-changepoint identification. Applied Statistics 43 (1994) 159-178
28. Tan M., Tian G.L., and Ng K.W. A noniterative sampling method for computing posteriors in the structure of EM-type algorithms. Statistica Sinica 13 (2003) 625-639
29. Tarr P.I., Neill M.A., Allen J., Siccardi C.J., Watkins S.L., and Hickman R.O. The increasing incidence of the hemolytic-uremic syndrome in King County Washington: Lack of evidence for ascertainment bias. American Journal of Epidemiol 129 3 (1989) 582-586
30. Worsley K.J. Confidence regions and tests for a change-point in a sequence of exponential family random variables. Biometrika 73 (1986) 91-104
31. Wu Y.H. Inference for Change Point and Post Change Means After a CUSUM Test. Lecture Notes in Statistics vol. 180 (2005), Springer, New York