Minimisation of bias of Pearson correlation coefficient in presence of coincidental outliers Online publication date: Mon, 04-Dec-2017
by Athanasios Tsagkanos
International Journal of Computational Economics and Econometrics (IJCEE), Vol. 8, No. 1, 2018
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'.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Economics and Econometrics (IJCEE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com