TY - JOUR
T1 - Generalized Neyman-Pearson optimality of empirical likelihood for testing parameter hypotheses
AU - Otsu, Taisuke
N1 - Funding Information:
T. Otsu would like to thank Yuichi Kitamura, Gustavo Soares, and two anonymous referees for helpful comments. Financial support from the National Science Foundation (SES-0720961) is gratefully acknowledged.
PY - 2009/12
Y1 - 2009/12
N2 - 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.
AB - 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.
KW - Empirical likelihood
KW - Generalized Neyman-Pearson optimality
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U2 - 10.1007/s10463-008-0172-6
DO - 10.1007/s10463-008-0172-6
M3 - Article
AN - SCOPUS:70449521474
SN - 0020-3157
VL - 61
SP - 773
EP - 787
JO - Annals of the Institute of Statistical Mathematics
JF - Annals of the Institute of Statistical Mathematics
IS - 4
ER -