An algorithmic method for detecting bunching in the value distribution of related variables - Katsaros, Ioannis; Karamanis, Kostas; Kolias, Georgios (2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 2021)
In this paper we present a proposed algorithm based on the bunching technique, which can be used sustainably in unobserved distributions. Automating bunching estimation is yet to receive substantial attention from the research community and our method will be one of the firsts attempts in the subject. The purpose of the algorithm is to be flexible, low-maintenance and generalized, when it comes to results; it is structured in several steps, each dependent on the previous. The algorithm concludes with the calculation of the bunching mass, if found, and based on the data distribution around that point, will calculate a significance measure. The algorithm is part of an end-product; an open-source programming language package (R/Python), to be used by the community.