| Title |
Application of the empirical Bayes approach to nonparametric testing for high-dimensional data / |
| Another Title |
Empirinio Bajeso metodo taikymas didelio matavimo duomenų neparametriniams testams. |
| Authors |
Jakimauskas, Gintautas ; Sušinskas, Jurgis |
| DOI |
10.15388/LMR.2010.73 |
| Full Text |
|
| Is Part of |
Lietuvos matematikos rinkinys. Lietuvos matematikų draugijos darbai. 2010, T. 51, p. 402-407.. ISSN 0132-2818 |
| Keywords [eng] |
Method, empirical Bayes ; Test, chi-square ; Data, high-dimensional ; Estimator, nonparametric maximum likelihood ; Testing, nonparametric ; Mean, posterior ; Simulations |
| Abstract [eng] |
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 χ^2-type test statistics. However, the χ^2 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. |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2010 |
