In this talk, we present maximum empirical likelihood estimation in the case of constraint functions that may be discontinuous and/or depend on additional parameters. The key to our analysis is a uniform local asymptotic normality condition for the local empirical likelihood ratio. This condition holds under mild assumptions and allows for a study of maximum empirical likelihood estimation and empirical likelihood ratio testing similar to that for parametric models. Applications of our results are discussed to inference problems about quantiles under possibly additional information on the underlying distribution and to partial adaption.
<a href="http://www.math.iupui.edu/~hpeng/">IUPUI Math</a>
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