In statistics, the RV coefficient is a multivariate generalization of the squared Pearson correlation coefficient (because the RV coefficient takes values between 0 and 1). It measures the closeness of two set of points that may each be represented in a matrix.
The major approaches within statistical multivariate data analysis can all be brought into a common framework in which the RV coefficient is maximised subject to relevant constraints. Specifically, these statistical methodologies include:
- principal component analysis
- canonical correlation analysis
- multivariate regression
- statistical classification (linear discrimination).
One application of the RV coefficient is in functional neuroimaging where it can measure the similarity between two subjects' series of brain scans or between different scans of a same subject.
Other articles related to "rv coefficient":
... Congruence coefficient Distance correlation. ...