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Empirical Likelihood Confidence Intervals for Linear Regression Coefficients

Song Chen

Journal of Multivariate Analysis, 1994, vol. 49, issue 1, 24-40

Abstract: 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.

Date: 1994
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Handle: RePEc:eee:jmvana:v:49:y:1994:i:1:p:24-40