RV coefficient

In statistics, the RV coefficient[1] is a multivariate generalization of the Pearson correlation coefficient. 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:[1]

One application of the RV coefficient is in functional neuroimaging where it can measure the similarity between two subjects' series of brain scans[2] or between different scans of a same subject.[3]

Definitions

The definition of the RV-coefficient makes use of ideas[4] concerning the definition of scalar-valued quantities which are called the "variance" and "covariance" of vector-valued random variables. Note that standard usage is to have matrices for the variances and covariances of vector random variables. Given these innovative definitions, the RV-coefficient is then just the correlation coefficient defined in the usual way.

Suppose that X and Y are matrices of centered random vectors (column vectors) with covariance matrix given by

\Sigma_{XY}=E( X^TY) \,,

then the scalar-valued covariance (denoted by COVV) is defined by[4]

\mathrm{COVV}(X,Y)= Tr(\Sigma_{XY}\Sigma_{YX}) \, .

The scalar-valued variance is defined correspondingly:

\mathrm{VAV}(X)= Tr(\Sigma_{XX}^2) \, .

With these definitions, the variance and covariance have certain additive properties in relation to the formation of new vector quantities by extending an existing vector with the elements of another.[4]

Then the RV-coefficient is defined by[4]

\mathrm{RV}(X,Y)=\frac { \mathrm{COVV}(X,Y) } { \sqrt{ \mathrm{VAV}(X) \mathrm{VAV}(Y) } } \, .

See also

References

  1. ^ a b Robert, P.; Escoufier, Y. (1976). "A Unifying Tool for Linear Multivariate Statistical Methods: The RV-Coefficient". Applied Statistics 25 (3): 257–265. doi:10.2307/2347233. JSTOR 2347233. 
  2. ^ Ferath Kherif; Jean-Baptiste Poline; Sébastien Mériaux; Habib Banali; Guillaume Plandin; Matthew Brett (2003). "Group analysis in functional neuroimaging: selecting subjects using similarity measures". NeuroImage 20 (4): 2197–2208. doi:10.1016/j.neuroimage.2003.08.018. PMID 14683722. 
  3. ^ Herve Abdi; Joseph P. Dunlop; Lynne J. Williams (2009). "How to compute reliability estimates and display confidence and tolerance intervals for pattern classiffers using the Bootstrap and 3-way multidimensional scaling (DISTATIS)". NeuroImage 45 (1): 89–95. doi:10.1016/j.neuroimage.2008.11.008. PMID 19084072. 
  4. ^ a b c d Escoufier, Y. (1973). "Le Traitement des Variables Vectorielles". Biometrics (International Biometric Society) 29 (4): 751–760. doi:10.2307/2529140. JSTOR 2529140.