Adaptive radial-based direction sampling: Some flexible and robust Monte Carlo integration methods

Journal of Econometrics

Volumn 123, Issue 2, 2004, Pages 201-225

  Bauwens, Luc ^a   Bos, Charles S ^b   Van Dijk, Herman K ^c,d   Van Oest, Rutger D ^c  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b VU UNIVERSITY AMSTERDAM   (Netherlands)

^c TINBERGEN INSTITUTE   (Netherlands)

^d ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

Author keywords

Importance sampling; Markov chain Monte Carlo; Radial coordinates

Indexed keywords

ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; MONTE CARLO METHODS; PROBABILITY; VECTORS;

IMPORTANCE SAMPLING; MARKOV CHAIN MONTE CARLO; RADIAL COORDINATES;

INTEGRATION;

EID: 4644327994     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2003.12.002     Document Type: Conference Paper

References (25)

1. Bauwens, L., Bos, C.S., Van Dijk, H.K., Van Oest, R.D., 2002. Adaptive polar sampling: a class of flexible and robust Monte Carlo integration methods. Working paper, Econometric Institute Erasmus University Rotterdam.
2. Box G.E.P., Muller M.E. A note on the generation of random normal deviates. Annals of Mathematical Statistics. 29:1958;610-611.
3. Brownlee K.A. Statistical Theory and Methodology in Science and Engineering. 2nd Edition:1965;Wiley, New York.
4. Chen M.-H., Shao Q.-M., Ibrahim J.G. Monte Carlo Methods in Bayesian Computation. 2000;Springer, New York.
5. Frühwirth-Schnatter S. Markov chain Monte Carlo estimation of classical and dynamic switching models. Journal of the American Statistical Association. 96:2001;194-209.
6. Geweke J. Bayesian inference in econometric models using Monte Carlo integration. Econometrica. 57:1989;1317-1339.
7. Geweke J. Using simulation methods for Bayesian econometric models. inference, development, and communication Econometric Reviews. 18:1999;1-73.
8. Gilks, W.R., Roberts, G.O., 1996. Strategies for improving MCMC. In: Gilks, W.R., Richardson, S., Spiegelhalter, D.J., (Eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall/CRC, New York, Boca Raton.
9. Gilks W.R., Roberts G.O., George E.I. Adaptive direction sampling. The Statistician. 43:1994;179-189.
10. Givens G.H., Raftery A.E. Local adaptive importance sampling for multivariate densities with strong nonlinear relationships. Journal of the American Statistical Association. 91:1996;132-141.
11. Hobert J.P., Casella G. The effect of improper priors on Gibbs sampling in hierarchical linear mixed models. Journal of the American Statistical Association. 91:1996;1461-1473.
12. Hop J.P., Van Dijk H.K. SISAM and MIXIN. Two algorithms for the computation of posterior moments and densities using Monte Carlo integration Computer Science in Economics and Management. 5:1992;183-220.
13. Justel A., Peña D. Gibbs sampling will fail in outlier problems with strong masking. Journal of Computational & Graphical Statistics. 5:1996;176-189.
14. Kleibergen F.R., Van Dijk H.K. On the shape of the likelihood/posterior in cointegration models. Econometric Theory. 10:1994;514-551.
15. Kleibergen F.R., Van Dijk H.K. Bayesian simultaneous equations analysis using reduced rank structures. Econometric Theory. 14:1998;701-743.
16. Koop G., Van Dijk H.K. Testing for integration using evolving trend and seasonal models. a Bayesian approach Journal of Econometrics. 97:2000;261-291.
17. Monahan J., Genz A. Spherical-radial integration rules for Bayesian computation. Journal of the American Statistical Association. 92:1997;664-674.
18. Muirhead R.J. Aspects of Multivariate Statistical Theory. 1982;Wiley, New York.
19. Oh M.S., Berger J.O. Adaptive importance sampling in Monte Carlo integration. Journal of Statistical Computation and Simulation. 41:1992;143-168.
20. Rubinstein R. Simulation and the Monte Carlo Method. 1981;Wiley, New York.
21. Schmeiser, B., Chen, M.-H., 1991. General hit-and-run Monte Carlo sampling for evaluating multidimensional integrals. Working paper, School of Industrial Engineering Purdue University.
22. Smith A.F.M., Roberts G.O. Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B. 55:1993;3-23.
23. Van Dijk H.K., Kloek T. Further experience in Bayesian analysis using Monte Carlo integration. Journal of Econometrics. 14:1980;307-328.
24. Van Dijk, H.K., Kloek, T., 1984. Experiments with some alternatives for simple importance sampling in Monte Carlo integration. In: Bernardo, J.M., Degroot, M., Lindley, D., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 2. Amsterdam, North Holland.
25. Van Dijk H.K., Kloek T., Boender C.G.E. Posterior moments computed by mixed integration. Journal of Econometrics. 29:1985;3-18.