ADJUSTED EMPIRICAL LIKELIHOOD WITH HIGH-ORDER PRECISION

By Yukun Liu, Jiahua Chen. 2010

101 citations recorded by the source corpus.

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 chi-square approximation and by nonexistence of solutions to the estimating equations. In this paper, 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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b33b9f769f81e5546a3c18fa16a66713cfffccbb

How researchers see this paper

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In Statistics and Probability

1. Fan Wu
2. Min Tsao
3. Songxi Chen
4. Jiahua Chen
5. I. Van Keilegom
6. Yukun Liu

Citations in the corpus

• Empirical likelihood on the full parameter space (2013, methodology): “Liu and Chen (2010) proved a surprising result that under a certain level of adjustment, the AEL confidence region achieves the second order accuracy of the Bartlett corrected empirical likelihood (BEL) region by DiCiccio, Hall and Romano (1991).”