Universal models for the exponential distribution

IEEE Transactions on Information Theory

Volumn 55, Issue 7, 2009, Pages 3087-3090

  Schmidt, Daniel F ^a   Makalic, Enes ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Minimum description length (MDL); Minimum message length (MML); Universal models

Indexed keywords

BAYES MIXTURES; BAYESIAN MIXTURE; DATA DISTRIBUTED; DATA DRIVEN; EXPONENTIAL DISTRIBUTIONS; MINIMUM DESCRIPTION LENGTH (MDL); MINIMUM MESSAGE LENGTH (MML); MINIMUM MESSAGE LENGTHS; NORMALIZED MAXIMUM LIKELIHOOD; UNIVERSAL MODEL; UNIVERSAL MODELS;

INFORMATION THEORY;

MAXIMUM LIKELIHOOD;

EID: 67650151803     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIT.2009.2018331     Document Type: Article

References (17)

1. J. Rissanen, Information and Complexity in Statistical Modeling, ser. Information Science and Statistics, 1st ed. NewYork: Springer, 2007.
2. J. Rissanen, "Fisher information and stochastic complexity," IEEE Trans. Inf. Theory, vol. 42, pp. 40-47, 1996.
3. J. Rissanen, "Strong optimality of the normalized ML models as universal codes and information in data," IEEE Trans. Inf. Theory vol. 47, pp. 1712-1717, 2001.
4. J. Rissanen, "Mdl denoising," IEEE Trans. Inf. Theory, vol. 46, pp. 2537-2543, 2000.
5. T. Roos and J. Rissanen, "On sequentially normalized maximum likelihood models," presented at the Workshop on Inf. Theoretic Methods in Sci. Eng. (WITMSE-08), Tampere, Finland, 2008.
6. J. Rissanen and T. Roos, "ConditionalNMLuniversal models," in Proc. 2007 Inf. Theory Appl. Workshop, 2007, pp. 337-341.
7. C. S. Wallace and D. M. Boulton, "An information measure for classification," Comp. J., vol. 11, no. 2, pp. 185-194, 1968.
8. C. Wallace and D. Boulton, "An invariant Bayes method for point estimation," Class. Soc. Bull., vol. 3, no. 3, pp. 11-34, 1975.
9. C. S. Wallace and P. R. Freeman, "Estimation and inference by compact coding," J. Royal Statist. Soc. (Series B), vol. 49, no. 3, pp. 240-252, 1987.
10. C. S. Wallace, Statistical and Inductive Inference by Minimum Message Length, ser. Information Science and Statistics, 1st ed. New York: Springer, 2005.
11. P. D. Grünwald, The Minimum Description Length Principle, ser. Adaptive Communication and Machine Learning. Cambridge, MA: The MIT Press, 2007.
12. J. Rissanen, "A predictive-least squares principle," IMA J. Math. Contr. Inf., vol. 3, pp. 211-222, 1986.
13. J. O. Berger and L. R. Pericchi, "Objective Bayesian methods for model selection: Introduction and comparison," Inst. Math. Statist. Lecture Notes (Monograph series), vol. 38, pp. 135-207, 1997.
14. M. H. Hansen and B. Yu, "Model selection and the principle of minimum description length," J. Amer. Statist. Assoc., vol. 96, no. 454, pp. 746-774, 2001.
15. F. Liang and A. Barron, "Exact minimax strategies for predictive density estimation, data compression, and model selection," IEEE Trans. Inf. Theory, vol. 50, pp. 2708-2726, Nov. 2004.
16. E. Makalic and D. F. Schmidt, "Minimum message length shrinkage estimation," Statist. Probab. Lett., vol. 79, no. 9, pp. 1155-1161, 2009.
17. S. Kullback and R. A. Leibler, "On information and sufficiency," The Ann. Math. Statist., vol. 22, no. 1, pp. 79-86, 1951.