2014/04/23 by Saumard, Adrien, Wellner, Jon A. · 13 citations
#60E15 #62E10 #62H05 #FOS: Mathematics #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.1404.5886
We review and formulate results concerning log-concavity and strong-log-concavity in both discrete and continuous settings. We show how preservation of log-concavity and strongly log-concavity on ℝ under convolution follows from a fundamental monotonicity result of Efron (1969). We provide a new proof of Efron's theorem using the recent asymmetric Brascamp-Lieb inequality due to Otto and Menz (2013). Along the way we review connections between log-concavity and other areas of mathematics and statistics, including concentration of measure, log-Sobolev inequalities, convex geometry, MCMC algorithms, Laplace approximations, and machine learning.