This paper studies the Generalized Neyman-Pearson (GNP) optimality of empirical likelihood-based tests for parameter hypotheses. The GNP optimality focuses on the large deviation errors of tests, i.e., the convergence rates of the type I and II error probabilities under fixed alternatives. We derive (i) the GNP optimality of the empirical likelihood criterion (ELC) test against all alternatives, and (ii) a necessary and a sufficient condition for the GNP optimality of the empirical likelihood ratio (ELR) test against each alternative.
|Number of pages||15|
|Journal||Annals of the Institute of Statistical Mathematics|
|Publication status||Published - 2009 Dec 1|
- Empirical likelihood
- Generalized Neyman-Pearson optimality
ASJC Scopus subject areas
- Statistics and Probability