Pool size selection for the sampling/importance resampling algorithm

Statistica Sinica

Volumn 17, Issue 3, 2007, Pages 895-907

  Li, Kim Hung ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Importance weight; Monte Carlo sampling; Resampling method; Sample size; Tight resampling algorithm

Indexed keywords

EID: 36248985198     PISSN: 10170405     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (22)

1. Albert, J. H. (1993). Teaching Bayesian statistics using sampling methods and MINITAB. Amer. Statist. 47, 182-191.
2. Berzuini, C., Best, N. G., Gilks, W. R. and Larizza, C. (1997). Dynamic conditional independence models and Markov chain Monte Carlo methods. J. Amer. Statist. Assoc. 92, 1403-1412.
3. Bunnin, F. O., Guo, Y. and Ren, Y. (2002). Option pricing under model and parameter uncertainty using predictive densities. Statist. Comput. 12, 37-44.
4. Davison, A. C. and Louzada-Neto, F. (2000). Inference for the poly-Weibull model. The Statistician 49, 189-196.
5. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85, 398-409.
6. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds.) (1996). Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
7. Gordon, N. J., Salmond, D. J. and Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F. 140, 107-113.
8. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Statist. 5, 1-25.
9. Koop, G. and Poirier, D. J. (2001). Testing for optimality in job search models. Econometrics J. 4, 257-272.
10. Lancaster, T. (1997). Exact structural inference in optimal job-search models. J. Bus. Econom. Statist. 15, 165-179.
11. Lee, D. (1997). Selecting sample sizes for the sampling/importance resampling filter. Proceedings of the Section on Bayesian Statistical Science, ASA, 72-77.
12. Li, K.-H. (2004). The sampling/importance resampling algorithm. In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. (Edited by A. Gelman and X.-L. Meng), 265-276. Wiley, London.
13. Lopes, H. F., Moreira, A. R. B. and Schmidt, A. M. (1999). Hyperparameter estimation in forecast models. Comput. Statist. Data Anal. 29, 387-410.
14. McAllister, M. K. and Ianelli, J. N. (1997). Bayesian stock assessment using catch-age data and the sampling-importance resampling algorithm. Canad. J. Fisheries and Aquatic Sci. 54, 284-300.
15. McAllister, M. K., Pikitch, E. K., Punt, A. E. and Hilborn, R. (1994). A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Canad. J. Fisheries and Aquatic Sci. 51, 2673-2687.
16. Newton, M. A. and Raftery, A. E. (1994). Approximate Bayesian inference with the weighted likelihood bootstrap (with discussion). J. Roy. Statist. Soe. Ser. B 56, 3-48.
17. Raftery, A. E., Givens, G. H. and Zeh, J. E. (1995). Inference from a deterministic population dynamics model for bowhead whales. J. Amer. Statist. Assoc. 90, 402-416.
18. Rubin, D. B. (1987). A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: the SIR algorithm. Discussion of Tanner and Wong (1987). J. Amer. Statist. Assoc. 82, 543-546.
19. Rubin, D. B. (1988). Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3 (Edited by J. M. Bernardo, M. H. DeGroot, D. V. Lindley, and A. F. M. Smith), 395-402. Oxford University Press, Oxford.
20. Skare, Ø., Bølviken, E. and Holden, L. (2003). Improved sampling-importance resampling and reduced bias importance sampling. Scand. J. Statist. 30, 719-737.
21. Smith, A. F. M. and Gelfand, A. E. (1992). Bayesian statistics without tears: a sampling-resampling perspective. Amer. Statist. 46, 84-88.
22. Tan, M., Tian, G.-L. and Ng, K. W. (2003). A noniterative sampling method for computing posteriors in the structure of EM-type algorithms. Statist. Sinica 13, 625-639.