Ricerc@Sapienza

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, that proves very useful in case of data far from
normality.