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Title: Weighted empirical and Euclidean likelihood covariate adjustment Authors:  Mihai Giurcanu - University of Chicago (United States) [presenting]
George Luta - Georgetown University (United States)
Gary Koch - University of North Carolina (United States)
Kirstine Amris - Copenhagen University Hospital (Denmark)
Pranab Sen - University of North Carolina (United States)
Abstract: Covariate adjustment is often used in the statistical analysis of randomized experiments to increase the efficiency of estimators of treatment effects. We study covariate adjustment based on the empirical and Euclidean likelihoods and propose weighted versions that arise as natural alternatives. The weighted methods incorporate the auxiliary information that the covariates have equal means among the treatment groups due to randomization. We show that the empirical and the Euclidean likelihoods and their weighted versions are the first-order equivalents to Koch's nonparametric covariance adjustment. Allowing the weights to be negative, the resulting pseudo-Euclidean likelihood is equivalent to Koch's method, and its weighted version can be viewed as a weighted version of Koch's method. In a simulation study, we assess the finite sample properties of the proposed methods. The analysis of a randomized clinical trial data set illustrates an application of these methods to a practical situation.