Speeding up lower bound estimation in powerlaw distributions

The traditional lower bound estimation method for powerlaw distributions based on the Kolmogorov-Smirnov distance proved to perform better than other competing methods. However, if applied to very large collections of data, such a method can be computationally demanding. In this paper, we propose two alternative methods with the aim to reduce the time required by the estimation procedure. We apply the traditional method and the two proposed methods to large collections of data ($N=500,000$) with varying values of the true lower bound. Both the proposed methods yield a significantly better performance and accuracy than the traditional method.