In recent years, density estimation via log-concave maximum likelihood estimation has emerged as a fascinating alternative to traditional nonparametric smoothing techniques, such as kernel density estimation, which require the choice of one or more bandwidths. I will outline some of the attractive properties of this technique, with a focus on new results on adaptivity properties and the estimation of high-dimensional log-concave densities.
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