February 2021 Adaptation in multivariate log-concave density estimation
Oliver Y. Feng, Adityanand Guntuboyina, Arlene K. H. Kim, Richard J. Samworth
Ann. Statist. 49(1): 129-153 (February 2021). DOI: 10.1214/20-AOS1950


We study the adaptation properties of the multivariate log-concave maximum likelihood estimator over three subclasses of log-concave densities. The first consists of densities with polyhedral support whose logarithms are piecewise affine. The complexity of such densities $f$ can be measured in terms of the sum $\Gamma(f)$ of the numbers of facets of the subdomains in the polyhedral subdivision of the support induced by $f$. Given $n$ independent observations from a $d$-dimensional log-concave density with $d\in\{2,3\}$, we prove a sharp oracle inequality, which in particular implies that the Kullback–Leibler risk of the log-concave maximum likelihood estimator for such densities is bounded above by $\Gamma(f)/n$, up to a polylogarithmic factor. Thus, the rate can be essentially parametric, even in this multivariate setting. For the second type of adaptation, we consider densities that are bounded away from zero on a polytopal support; we show that up to polylogarithmic factors, the log-concave maximum likelihood estimator attains the rate $n^{-4/7}$ when $d=3$, which is faster than the worst-case rate of $n^{-1/2}$. Finally, our third type of subclass consists of densities whose contours are well separated; these new classes are constructed to be affine invariant and turn out to contain a wide variety of densities, including those that satisfy Hölder regularity conditions. Here, we prove another sharp oracle inequality, which reveals in particular that the log-concave maximum likelihood estimator attains a risk bound of order $n^{-\min (\frac{\beta+3}{\beta+7},\frac{4}{7})}$ when $d=3$ over the class of $\beta$-Hölder log-concave densities with $\beta >1$, again up to a polylogarithmic factor.


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Oliver Y. Feng. Adityanand Guntuboyina. Arlene K. H. Kim. Richard J. Samworth. "Adaptation in multivariate log-concave density estimation." Ann. Statist. 49 (1) 129 - 153, February 2021. https://doi.org/10.1214/20-AOS1950


Received: 1 December 2018; Revised: 1 October 2019; Published: February 2021
First available in Project Euclid: 29 January 2021

Digital Object Identifier: 10.1214/20-AOS1950

Primary: 62G07 , 62G20

Keywords: Bracketing entropy , contour separation , Log-concavity , maximum likelihood estimation , Multivariate adaptation

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.49 • No. 1 • February 2021
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