Open AccessApplication of the empirical Bayes approach to nonparametric testing for high-dimensional data
Author(s) -
Gintautas Jakimauskas,
Jurgis Sušinskas
Publication year - 2010
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.2010.73
Subject(s) - nonparametric statistics , resampling , statistics , statistical hypothesis testing , bayes' theorem , estimator , mathematics , computer science , type i and type ii errors , bayes factor , bayesian probability
In [5] a simple, data-driven and computationally efficient procedure of (nonparametric) testing for high-dimensional data have been introduced. The procedure is based on randomization and resampling, a special sequential data partition procedure, and χ2-type test statistics. However, the χ2 test has small power when deviations from the null hypothesis are small or sparse. In this note test statistics based on the nonparametric maximum likelihood and the empirical Bayes estimators.