  We prove a uniform in bandwidth law of the iterated logarithm for the maximal
deviation of kernel copula estimators from their expectations. We deal
especially with the \textit{local linear}, the \textit{mirror-reflection} and
the \textit{transformation} estimators. These results are useful for
establishing the strong uniform in bandwidth consistency of these kernel
estimators.
