The paper is concerned with testing uniformity versus a monotone
den-sity. This problem arises in two important contexts, after transformations,
testing whether a sample is a simple random sample or a biased sample, and
testing whether the intensity function of a nonhomogeneous Poisson process is
constant against monotone alternatives. A penalized likelihood ratio test
($P$-test) and a Dip likelihood test ($D$-test) are developed. The $D$-test is
analogous to Hartigan and Hartigan’s (1985) Dip test for bump hunting
problems. While nonparametric, both the $P$- and $D$-tests are quite efficient
in comparison to the most powerful (MP) tests for some simple alternatives and
also the Laplace test developed for nonhomogeneous Poisson process. The $P$-
and $D$-tests have higher power than the above MP tests under different sets of
monotone alternatives and so have greater applicability. Moderate sample size
performance and applications of our tests are illustrated via simulations and
examination of an air-conditioning equipment data set.
@article{1018031114,
author = {Woodroofe, Michael and Sun, Jiayang},
title = {Testing uniformity versus a monotone density},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 338-360},
language = {en},
url = {http://dml.mathdoc.fr/item/1018031114}
}
Woodroofe, Michael; Sun, Jiayang. Testing uniformity versus a monotone density. Ann. Statist., Tome 27 (1999) no. 4, pp. 338-360. http://gdmltest.u-ga.fr/item/1018031114/