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

Speeding up lower bound estimation in powerlaw distributions