Empirical likelihood for outlier detection in regression models

Outlier detection and treatment are important steps in exploratory data analysis. A case deletion method in the empirical likelihood framework is suggested here for outlier detection in regression models. The theoretical properties of empirical likelihood hypothesis testing for outlier detection are investigated and asymptotic results are obtained and compared to the empirical likelihood displacement measure. The behavior of our test statistics in finite samples is studied by means of an extensive simulation experiment and some real data sets. A bootstrap version of the test is also proposed, which proves very useful in the case of data far from normality.