Rank Correlation Methods

By M. Kendall. 1949

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How algorithmic popularity bias hinders or promotes quality (2017, methodology): “ property ˝ is the faithfulness of the algorithm, i.e., the degree to which quality is re ected in popularity. We quantify faithfulness using Kendall’s rank correlation between popularity and quality [17]. We can derive the values of both properties in the extreme cases of maximum or no popularity b”
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning (2019, methodology): “s gives an indication of value loss amongst all good actions). Finally, we compute the number of correctly ranked actions (accounting for ties were needed) and the corresponding Kendall ˝-b coecient [13] between the computed ranking and the original one. For every method, mean values and standard er”
Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage (2008, unknown): “Spearman, Kendall (Kendall, 1970) or the weighted Spearman measure given in Clarke and Ainsworth (1993) are among the possibilities here.”