Gijbels, I.; Hall, P.; Jones, M. C. and Koch, I.
|Google Scholar:||Look up in Google Scholar|
In this paper a nonparametric procedure for testing for monotonicity of a regression mean with guaranteed level is proposed. The procedure is based on signs of differences of observations from the response variable. The test is calibrated against the most difficult null hypothesis, when the regression function is constant, and produces an exact test in this context. In general, the test is conservative. The power of the test is good, and comparable with that of other nonparametric tests. It is shown that the testing procedure has asymptotic power 1 against certain local alternatives. The method is also robust against heavy-tailed error distributions, and even maintains good power when the errors are for example Cauchy distributed. A simulation study is provided to demonstrate finite-sample behaviour of the testing procedure.
|Item Type:||Journal Article|
|Copyright Holders:||2000 Biometrika Trust|
|Keywords:||calibration; counts; exact test; lengths of runs; local alternative; Monte Carlo simulation|
|Academic Unit/Department:||Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
|Depositing User:||Sarah Frain|
|Date Deposited:||05 Apr 2011 13:50|
|Last Modified:||15 Jan 2016 15:00|
|Share this page:|