Extreme value theory is focused on developing methods for tail inference where data are scarce and central measures may not be suitable. Such is the case of correlation to assess linear association between two variables. For example, the bivariate Gaussian model always shows an asymptotic tail independence, no matter how strong the correlation is. In this work we address the tail independence coefficient \(\eta \) of Ledford and Tawn, a measure to assess the presence of an extremal residual dependence. It can be estimated as a regular variation index, for which several estimators already exist. A major problem is the selection of the optimal sample fraction to be considered in the estimation. Based on a simulation study, we will make a comparative analysis of different methodologies adapted to the estimation of \(\eta \) . We will finish with an illustration in real data.