The Annals of Statistics

From ɛ-entropy to KL-entropy: Analysis of minimum information complexity density estimation

Tong Zhang

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Abstract

We consider an extension of ɛ-entropy to a KL-divergence based complexity measure for randomized density estimation methods. Based on this extension, we develop a general information-theoretical inequality that measures the statistical complexity of some deterministic and randomized density estimators. Consequences of the new inequality will be presented. In particular, we show that this technique can lead to improvements of some classical results concerning the convergence of minimum description length and Bayesian posterior distributions. Moreover, we are able to derive clean finite-sample convergence bounds that are not obtainable using previous approaches.

Article information

Source
Ann. Statist. Volume 34, Number 5 (2006), 2180-2210.

Dates
First available in Project Euclid: 23 January 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1169571794

Digital Object Identifier
doi:10.1214/009053606000000704

Mathematical Reviews number (MathSciNet)
MR2291497

Zentralblatt MATH identifier
1106.62005

Subjects
Primary: 62C10: Bayesian problems; characterization of Bayes procedures 62G07: Density estimation

Keywords
Bayesian posterior distribution minimum description length density estimation

Citation

Zhang, Tong. From ɛ -entropy to KL-entropy: Analysis of minimum information complexity density estimation. Ann. Statist. 34 (2006), no. 5, 2180--2210. doi:10.1214/009053606000000704. http://projecteuclid.org/euclid.aos/1169571794.


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