Common canonical variates for independent groups using information theory
|Title||Common canonical variates for independent groups using information theory|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Yin X, Sriram TN|
Suppose that data on (X, Y), where X is a q x 1 vector and Y is p x I vector, are collected from C independent but closely related populations, and that one is interested in measuring the amount of relationship between sets of variables Y and X within each population. Goria and Flury (1996) argued that in these situations it is more meaningful to construct common canonical variates that are identical across populations, while the canonical correlations themselves may vary. Here we construct common information canonical variates based on Kullback-Leibler information. The proposed method does not require specific distributional assumptions and is useful in measuring true relations, whether linear or nonlinear. Simulations and dataset examples are presented. We also contrast our findings, in some instances, with those of Goria and Flury (1996).