Inference and Modeling with Log-concave Distributions

Details Inference and Modeling with Log-concave Distributions Inference and Modeling with Log-concave Distributions

2010-10-02

arxiv.org/abs/1010.0305v1

Statistical Science 2009, Vol. 24, No. 3, 319-327

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/09-STS303 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M

Scientific paper

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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