Model Selection and the Principle of Minimum Description Length
Top Cited Papers
- 1 June 2001
- journal article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 96 (454) , 746-774
- https://doi.org/10.1198/016214501753168398
Abstract
This article reviews the principle of minimum description length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the M...Keywords
This publication has 61 references indexed in Scilit:
- Multiple shrinkage and subset selection in waveletsBiometrika, 1998
- Nonparametric regression using Bayesian variable selectionJournal of Econometrics, 1996
- Data compression and histogramsProbability Theory and Related Fields, 1992
- Density estimation by stochastic complexityIEEE Transactions on Information Theory, 1992
- Information-theoretic asymptotics of Bayes methodsIEEE Transactions on Information Theory, 1990
- A note on some model selection criteriaStatistics & Probability Letters, 1986
- A source matching approach to finding minimax codesIEEE Transactions on Information Theory, 1980
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974
- Universal noiseless codingIEEE Transactions on Information Theory, 1973