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- Publisher
- Springer Journals
- Copyright
- Copyright © 2009 by Springer Science+Business Media, LLC
- Subject
- Statistics; Statistics, general; Life Sciences, general; Electrical Engineering; Economics, general; Business and Management, general
- ISSN
- 1387-5841
- eISSN
- 1573-7713
- DOI
- 10.1007/s11009-009-9133-7
- Publisher site
- See Article on Publisher Site
Abstract
Nonparametric density estimation aims to determine the sparsest model that explains a given set of empirical data and which uses as few assumptions as possible. Many of the currently existing methods do not provide a sparse solution to the problem and rely on asymptotic approximations. In this paper we describe a framework for density estimation which uses information-theoretic measures of model complexity with the aim of constructing a sparse density estimator that does not rely on large sample approximations. The effectiveness of the approach is demonstrated through an application to some well-known density estimation test cases.
Journal
Methodology and Computing in Applied Probability – Springer Journals
Published: May 16, 2009