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February 2000 Testing for monotonicity of a regression mean by calibrating for linear functions
Peter Hall, Nancy E. Heckman
Ann. Statist. 28(1): 20-39 (February 2000). DOI: 10.1214/aos/1016120363


A new approach to testing for monotonicity of a regression mean, not requiring computation of a curve estimator or a bandwidth, is suggested. It is based on the notion of “running gradients ” over short intervals, although from some viewpoints it may be regarded as an analogue for monotonicity testing of the dip/excess mass approach for testing modality hypotheses about densities. Like the latter methods, the new technique does not suffer difficulties caused by almost-flat parts of the target function. In fact, it is calibrated so as to work well for flat response curves, and as a result it has relatively good power properties in boundary cases where the curve exhibits shoulders. In this respect, as well as in its construction, the “running gradients” approach differs from alternative techniques based on the notion of a critical bandwidth.


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Peter Hall. Nancy E. Heckman. "Testing for monotonicity of a regression mean by calibrating for linear functions." Ann. Statist. 28 (1) 20 - 39, February 2000.


Published: February 2000
First available in Project Euclid: 14 March 2002

zbMATH: 1106.62324
MathSciNet: MR1762902
Digital Object Identifier: 10.1214/aos/1016120363

Primary: 62F40 , 62G08 , G2F30

Keywords: bootstrap , Calibration , curve stimation , Monte Carlo , response curve , running gradient

Rights: Copyright © 2000 Institute of Mathematical Statistics


Vol.28 • No. 1 • February 2000
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