Breakdown Analysis of Pearson Correlation Coefficient and Robust Correlation Methods

Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander's overlap coefficient.

The Pearson correlation coefficient (PCC) and the Mander's overlap coefficient (MOC) are used to quantify the degree of colocalization between fluorophores. The MOC was introduced to overcome perceived problems with the PCC. The two coefficients are mathematically similar, differing in the use of either the absolute intensities (MOC) or of the deviation from the mean (PCC). A range of correlate...

A new consensus measure based on Pearson correlation coefficient

Obtaining consensual solutions is an important issue in decision making processes. It depends on several factors such as experts’ opinions, principles, knowledge, experience, etc. In the literature we can find a considerable amount of consensus measurement from different research areas (from a Social Choice perspective: Alcalde-Unzu and Vorsatz [1], Alcantud, de Andrés Calle and Cascón [2] and ...

Robust Methods for Canonical Correlation Analysis

Canonical correlation analysis studies associations between two sets of random variables. Its standard computation is based on sample covariance matrices, which are however very sensitive to outlying observations. In this note we introduce, discuss and compare four different ways for performing a robust canonical correlation analysis. One method uses robust estimators of the involved covariance...

Title The Phi - coefficient , the Tetrachoric Correlation Coefficient , and the Pearson - Yule Debate

Two measures of association for dichotomous variables, the phi-coefficient and the tetrachoric correlation coefficient, are reviewed and differences between the two are discussed in the context of the famous so-called Pearson-Yule debate, that took place in the early 20th century. The two measures of association are given mathematically rigorous definitions, their underlying assumptions are for...

Pearson Product-Moment Correlation