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2019 Univariate log-concave density estimation with symmetry or modal constraints
Charles R. Doss, Jon A. Wellner
Electron. J. Statist. 13(2): 2391-2461 (2019). DOI: 10.1214/19-EJS1574

Abstract

We study nonparametric maximum likelihood estimation of a log-concave density function $f_{0}$ which is known to satisfy further constraints, where either (a) the mode $m$ of $f_{0}$ is known, or (b) $f_{0}$ is known to be symmetric about a fixed point $m$. We develop asymptotic theory for both constrained log-concave maximum likelihood estimators (MLE’s), including consistency, global rates of convergence, and local limit distribution theory. In both cases, we find the MLE’s pointwise limit distribution at $m$ (either the known mode or the known center of symmetry) and at a point $x_{0}\ne m$. Software to compute the constrained estimators is available in the R package logcondens.mode.

The symmetry-constrained MLE is particularly useful in contexts of location estimation. The mode-constrained MLE is useful for mode-regression. The mode-constrained MLE can also be used to form a likelihood ratio test for the location of the mode of $f_{0}$. These problems are studied in separate papers. In particular, in a separate paper we show that, under a curvature assumption, the likelihood ratio statistic for the location of the mode can be used for hypothesis tests or confidence intervals that do not depend on either tuning parameters or nuisance parameters.

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Charles R. Doss. Jon A. Wellner. "Univariate log-concave density estimation with symmetry or modal constraints." Electron. J. Statist. 13 (2) 2391 - 2461, 2019. https://doi.org/10.1214/19-EJS1574

Information

Received: 1 October 2018; Published: 2019
First available in Project Euclid: 23 July 2019

zbMATH: 1422.62137
MathSciNet: MR3983344
Digital Object Identifier: 10.1214/19-EJS1574

Subjects:
Primary: 62G07
Secondary: 62G05, 62G20

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Vol.13 • No. 2 • 2019
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