Model Selection and the Principleof Minimum Description

  title={Model Selection and the Principleof Minimum Description},
  author={L H Hansen and Bin Yu},
This paper 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 MDL framework discriminates between competing models based on the complexity of each description. This approach began with Kolmogorov's theory of algorithmic complexity, matured in the literature on information theory, and has recently received renewed interest within the statistics community. In the… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 88 references

Some Comments on Cp

View 4 Excerpts
Highly Influenced

The Minimum Description Length Principle in Coding and Modeling

IEEE Trans. Information Theory • 1998
View 19 Excerpts
Highly Influenced

MDL and MML: Similarities and Di erences (Introduction to minimum

R. Baxter, J. Oliver
View 4 Excerpts
Highly Influenced

Time series: theory and methods

P. J. Brockwell, R. A. Davis
New York: Springer-Verlag. • 1991
View 5 Excerpts
Highly Influenced

Information Theoretical Methods in Statistics

I. ar
Class notes, University of Maryland, • 1990
View 12 Excerpts
Highly Influenced

A new look at the statistical model identi cation

H. Akaike
IEEE Trans. AC, 19 716-723. • 1974
View 11 Excerpts
Highly Influenced