Given a set of data drawn from an unknown density, it is frequently desirable to estimate the number and location of modes of the density. A test is proposed for the weight of evidence of individual observed modes. The test statistic used is a measure of the size of the mode, the absolute integrated difference between the estimated density and the same density with the mode in question excised at the level of the higher of its two surrounding antimodes. Samples are simulated from a conservative member of the composite null hypothesis to estimate p-values within a Monte Carlo setting. Such a test can be used with the graphical "mode tree" of Minnotte and Scott to examine, in a locally adaptive fashion, not only the reality of individual modes, but also (roughly) the overall number of modes of the density. A proof of consistency of the test statistic is offered and simulation results are presented.
Publié le : 1997-08-14
Classification:
Bump hunting,
kernel density estimation,
mode estimation,
multi-modality,
62G10,
62G07,
62G09,
62G20
@article{1031594735,
author = {Minnotte, Michael C.},
title = {Nonparametric testing of the existence of modes},
journal = {Ann. Statist.},
volume = {25},
number = {6},
year = {1997},
pages = { 1646-1660},
language = {en},
url = {http://dml.mathdoc.fr/item/1031594735}
}
Minnotte, Michael C. Nonparametric testing of the existence of modes. Ann. Statist., Tome 25 (1997) no. 6, pp. 1646-1660. http://gdmltest.u-ga.fr/item/1031594735/