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Fei Tan

Indiana University-Purdue University Indianapolis



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Hanxiang Peng

Indiana University-Purdue University Indianapolis



497 – Nonparametric

Improved M-Estimates in Convex Minimization �� An Easy Empirical Likelihood Approach

Sponsor: IMS
Keywords: convex function, Cox regression, efficiency, maximum empirical likelihood estimator, quantile regresion, side information

Fei Tan

Indiana University-Purdue University Indianapolis

Hanxiang Peng

Indiana University-Purdue University Indianapolis

In this talk, we introduce a class of estimators defined by convex criterion functions and show that they are maximum empirical likelihood estimators (MELEs). We apply the results to obtain MELEs for quantiles, quantile regression and Cox regression when additional information is available. We report some simulation results.

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