Application of the empirical Bayes approach to nonparametric testing for high-dimensional data
Articles
Gintautas Jakimauskas
Matematikos ir informatikos institutas
Jurgis Sušinskas
Matematikos ir informatikos institutas
Published 2010-12-21
https://doi.org/10.15388/LMR.2010.73
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Keywords

empirical Bayes
chi-square test
high-dimensional data
nonparametric maximum likelihood estimator
nonparametric testing
posterior mean
simulations

How to Cite

Jakimauskas G. and Sušinskas J. (2010) “Application of the empirical Bayes approach to nonparametric testing for high-dimensional data”, Lietuvos matematikos rinkinys, 51(proc. LMS), pp. 402–407. doi: 10.15388/LMR.2010.73.

Abstract

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.

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