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Testing monotonicity of regression

Bowman, A. W.; Jones, M. C. and Gijbels, I. (1998). Testing monotonicity of regression. Journal of Computational and Graphical Statistics, 7(4) pp. 489–500.

URL: http://www.jstor.org/stable/1390678
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Abstract

This article provides a test of monotonicity of a regression function. The test is based on the size of a "critical" bandwidth, the amount of smoothing necessary to force a nonparametric regression estimate to be monotone.It is analogous to Silverman's test of multimodality in density estimation. Bootstrapping is used to provide a null distribution for the test statistic. The methodology is particularly simple in regression models in which the variance is a specified function of the mean, but we also discuss in detail the homoscedastic case with unknown variance. Simulation evidence indicates the usefulness of the method. Two examples are given.

Item Type: Journal Article
Copyright Holders: 1998 American Statistical Association , Institute of Mathematical Statistics
ISSN: 1537-2715
Keywords: bootstrap; critical bandwidth; local linear fitting; multimodality testing; smoothing
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Item ID: 24028
Depositing User: Sarah Frain
Date Deposited: 04 May 2011 13:21
Last Modified: 04 May 2011 13:21
URI: http://oro.open.ac.uk/id/eprint/24028
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