An extended empirical likelihood approach to improved estimation

BONG-JIN CHOI, Mingao Yuan

Abstract


The extended empirical likelihood is proposed recently to improve the
coverage accuracy of empirical likelihood ratio condence region. In this
paper, we use the extended empirical likelihood(EEL) to incorporate side
information to improve the eciency of the empirical estimator of some linear
functional. We get the asymptotic normality of the EEL-weighted estimator
and our simulation study shows that the EEL-weighted estimator performs
better than the usual empirical likelihood(EL) weighted estimator and the
empirical estimator, especially when the sample size is small.


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