Abstract
A new weighted rank correlation coefficient, \(r_{W2}\), has been introduced in Pinto da Costa, Weighted Correlation, 2011, [74] and applied in a bioinformatics context in Pinto da Costa et al., IEEE/ACM Trans Comput Biol Bioinf 8(1):246–252, 2011, [73]. This coefficient is the second of its series, following the coefficient \(r _{W}\) introduced in Pinto da Costa et al., Nonlinear Estimation and Classification, MSRI, 2001 [61], Pinto da Costa and Soares, Australian New Zealand J Stat 47(4):515–529, 2005, [63], Soares et al., JOCLAD 2001: VII Jornadas de Classificação e Análise de Dados, Porto, 2001, [93], which was motivated by a machine learning problem concerning the recommendation of learning algorithms. These coefficients were inspired by Spearman’s rank correlation coefficient, \(r_S\). Nevertheless, unlike Spearman’s, which treats all ranks equally, these coefficients weigh the distance between two ranks using a linear function of those ranks in the case of \(r_W\) and a quadratic function in the case of \(r_{W2}\). In both cases, these functions give more importance to top ranks than lower ones, although \(r_{W2}\) has some advantages over \(r_W\) as we will see. In some of the applications of weighted correlation, ties can happen naturally; nevertheless, the existing coefficients tend to ignore this situation. We give here the expression of \(r_{W2}\) in the case of ties. We present also some simulations in order to compare the three coefficients \(r_{W2}\), \(r_W\), and \(r_S\).
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Pinto da Costa, J. (2015). The Weighted Rank Correlation Coefficient \(r_{W2}\) . In: Rankings and Preferences. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48344-2_3
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DOI: https://doi.org/10.1007/978-3-662-48344-2_3
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