  In this manuscript we discuss the effectiveness of the Kozachenko-Leonenko
entropy estimator when generalised to cope with entropic forms customarily
applied to study systems evincing asymptotic scale invariance and dependence
(either linear or non-linear type). We show that when the variables are
independently and identically distributed the estimator is only valuable along
the whole domain if the data follow the uniform distribution, whereas for other
distributions the estimator is only effectual in the limit of the
Boltzmann-Gibbs-Shanon entropic form. We also analyse the influence of the
dependence (linear and non-linear) between variables on the accuracy of the
estimator between variables. As expected in the last case the estimator looses
efficiency for the Boltzmann-Gibbs-Shanon entropic form as well.
