Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Minimisation of bias of Pearson correlation coefficient in presence of coincidental outliers 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'. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

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

Loading next page...
 
/lp/inderscience-publishers/minimisation-of-bias-of-pearson-correlation-coefficient-in-presence-of-C0rAL8GYTC
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2018.088308
Publisher site
See Article on Publisher Site

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'.

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2018

There are no references for this article.