Efficient Estimation of the Canonical Dependence Function

The canonical dependence function (z), z [0,1], is introduced and studied in detail for distributions, which belong to the -neighborhood of a bivariate generalized Pareto distribution. We establish local asymptotic normality (LAN) of the loglikelihood function of a 2×2 table sorting of n i.i.d. observations and derive efficient estimators of (z) from the Hájek-LeCam Convolution Theorem. These results extend results by Falk and Reiss (2003) for the canonical dependence parameter (1/2) to arbitrary z (0,1).