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August 2009 Inference and Modeling with Log-concave Distributions
Guenther Walther
Statist. Sci. 24(3): 319-327 (August 2009). DOI: 10.1214/09-STS303


Log-concave distributions are an attractive choice for modeling and inference, for several reasons: The class of log-concave distributions contains most of the commonly used parametric distributions and thus is a rich and flexible nonparametric class of distributions. Further, the MLE exists and can be computed with readily available algorithms. Thus, no tuning parameter, such as a bandwidth, is necessary for estimation. Due to these attractive properties, there has been considerable recent research activity concerning the theory and applications of log-concave distributions. This article gives a review of these results.


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Guenther Walther. "Inference and Modeling with Log-concave Distributions." Statist. Sci. 24 (3) 319 - 327, August 2009.


Published: August 2009
First available in Project Euclid: 31 March 2010

zbMATH: 1329.62192
MathSciNet: MR2757433
Digital Object Identifier: 10.1214/09-STS303

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.24 • No. 3 • August 2009
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