  The (relevance) weighted likelihood was introduced to formally embrace a
variety of statistical procedures that trade bias for precision. Unlike its
classical counterpart, the weighted likelihood combines all relevant
information while inheriting many of its desirable features including good
asymptotic properties. However, in order to be effective, the weights involved
in its construction need to be judiciously chosen. Choosing those weights is
the subject of this article in which we demonstrate the use of
cross-validation. We prove the resulting weighted likelihood estimator (WLE) to
be weakly consistent and asymptotically normal. An application to disease
mapping data is demonstrated.
