You can view the full text of this article for free using the link below.

Title: Minimisation of bias of Pearson correlation coefficient in presence of coincidental outliers

Authors: Athanasios Tsagkanos

Addresses: Department of Business Administration, University of Patras, University Campus – Rio, P.O. Box 1391, Patras 26500, Greece

Abstract: It is well known that sample correlation coefficient is a significant statistical measure of linear comovement between variables. However, the distortion that is caused by 'coincidental outliers' is fairly large. For this reason, we suggest an alternative robust measure of correlation that obtains the lowest bias. We formally call this measure the bootstrap-based correlation coefficient. We show analytically that our measure exhibits lower bias with respect to classical estimator. We compare its performance both across the classical estimator and across the robust measures of Kim et al. (2015) applying Monte Carlo simulations. The results verify the outperformance of the bootstrap-based correlation coefficient relatively to other measures, in presence of 'coincidental outliers'.

Keywords: correlation coefficient; coincidental outliers; bias; bootstrap.

DOI: 10.1504/IJCEE.2018.088308

International Journal of Computational Economics and Econometrics, 2018 Vol.8 No.1, pp.121 - 128

Available online: 18 Sep 2017 *

Full-text access for editors Access for subscribers Free access Comment on this article