Adjusted
Empirical Likelihood with High-Order Precision
Jiahua Chen (University of British Columbia)
9/03/10
Empirical
likelihood is a popular nonparametric or semi-parametric statistical
method with many nice statistical properties. Yet when the sample size
is small, or the dimension of the accompanying estimating function is
high, the application of the empirical likelihood method can be
hindered by low precision of the chisquare approximation and by
non-existence of solutions to the estimating equations. In this talk,
we show that the adjusted empirical likelihood is effective at
addressing both problems. With a specific level of adjustment, the
adjusted empirical likelihood achieves the high-order precision of the
Bartlett correction, in addition to the advantage of a guaranteed
solution to the estimating equations. Simulation results indicate that
the confidence regions constructed by the adjusted empirical likelihood
have coverage probabilities comparable to or substantially more
accurate than the original empirical likelihood enhanced by the
Bartlett correction.
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[Last modified: Mar. 8th 2010 by Kostas
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