Empirical Likelihood Confidence Intervals for Linear Regression Coefficients

Nonparametric versions of Wilks′ theorem are proved for empirical likelihood estimators of slope and mean parameters for a simple linear regression model. They enable us to construct empirical likelihood confidence intervals for these parameters. The coverage errors of these confidence intervals are of order n−1 and can be reduced to order n−2 by Bartlett correction.