Robust estimation and outlier detection with correlation coefficients

Abstract

Two graphical methods are proposed for identifying bivariate observations that may unduly influence the sample correlation coefficient. Secondly, robust estimators of correlation are developed and a Monte Carlo comparative study is made of these and other well-known estimators. Also considered are methods for developing positive-definite estimates of correlation matrices and extensions of robustness to other problems such as regression are mentioned.