Assessing the Effect of Individual Data Points on Inference
from Empirical Likelihood
Nicole A. Lazar
An oft-cited advantage of empirical likelihood is that
the confidence intervals that are produced by this
non-parametric technique are not necessarily symmetric.
Rather, they reflect the nature of the underlying data
and hence give a more representative way of reaching
inferences about the functional of interest. However,
this advantage can easily become a disadvantage if the
resultant intervals are unduly influenced by some of the
data points. In this paper, we consider the effect of
extreme points, not necessarily outliers, on the profile
empirical likelihood ratio and on
empirical likelihood confidence intervals.
In addition to suggesting diagnostics
for detecting important observations, we examine
the use of bootstrap and of jackknife influence
functions to assess the extremity of suspect points.