Bernoulli

Adaptive risk bounds in unimodal regression

Sabyasachi Chatterjee and John Lafferty

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Abstract

We study the statistical properties of the least squares estimator in unimodal sequence estimation. Although closely related to isotonic regression, unimodal regression has not been as extensively studied. We show that the unimodal least squares estimator is adaptive in the sense that the risk scales as a function of the number of values in the true underlying sequence. Such adaptivity properties have been shown for isotonic regression by Chatterjee et al. (Ann. Statist. 43 (2015) 1774–1800) and Bellec (Sharp oracle inequalities for Least Squares estimators in shape restricted regression (2016)). A technical complication in unimodal regression is the non-convexity of the underlying parameter space. We develop a general variational representation of the risk that holds whenever the parameter space can be expressed as a finite union of convex sets, using techniques that may be of interest in other settings.

Article information

Source
Bernoulli, Volume 25, Number 1 (2019), 1-25.

Dates
Received: July 2016
Revised: December 2016
First available in Project Euclid: 12 December 2018

Permanent link to this document
https://projecteuclid.org/euclid.bj/1544605234

Digital Object Identifier
doi:10.3150/16-BEJ922

Mathematical Reviews number (MathSciNet)
MR3892309

Zentralblatt MATH identifier
07007197

Keywords
isotonic regression minimax bounds shape constrained inference unimodal regression

Citation

Chatterjee, Sabyasachi; Lafferty, John. Adaptive risk bounds in unimodal regression. Bernoulli 25 (2019), no. 1, 1--25. doi:10.3150/16-BEJ922. https://projecteuclid.org/euclid.bj/1544605234


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