Univariate log-concave density estimation with symmetry or modal constraints

TL;DR

This paper develops asymptotic theory for constrained log-concave density estimators, specifically when the mode or symmetry is known, providing tools for location estimation, mode-regression, and hypothesis testing.

Contribution

It introduces the first asymptotic analysis of constrained log-concave MLEs with known mode or symmetry, including consistency, convergence rates, and limit distributions.

Findings

Derived pointwise limit distributions at the mode and other points

Established consistency and convergence rates for the estimators

Provided software implementation in R package logcondens.mode

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 \verb+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.