  We consider regularly varying random vectors. Our goal is to estimate in a
non-parametric way some characteristics related to conditioning on an extreme
event, like the tail dependence coefficient. We introduce a quasi-spectral
decomposition that allow to improve efficiency of estimators. Asymptotic
normality of estimators is based on weak convergence of tail empirical
processes. Theoretical results are supported by simulation studies.
