Canonical Correlation: Permutation Tests and Regression

Multi-view Regression Via Canonical Correlation Analysis

In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assumed that either view of the input is sufficient to make accurate predictions — this is essentially (a significantly weaker version of) the co-training assumption for the regression problem. We provide a semi-supervised ...

Permutation versus Bootstrap Significance Tests in Multiple Regression and Anova

Kempthorne's (1952) formulation of the randomization test is extended to yield a permutational analog of the bootstrap significance test. In the new test, residuals of a multiple regression are permuted instead of being bootstrapped. The test is an attractive alternative for Oja's test that permutes predictors (Austr. J.

Permutation Tests for Correlation between Two Distance Matrices

Biologists frequently summarize multivariate data from n populations by computing some measure of distance between populations. The problem then arises of comparing two such pairwise distance matrices based on different characters. A permutation test for correlation between distance matrices is proposed. This test, based on Kendall's tau statistic, is compared to Pearson's product-moment and Sp...

Multivariate and Multiple Permutation Tests

In this article, we consider the use of permutation tests for comparing multivariate parameters from two populations. First, the underlying properties of permutation tests when comparing parameter vectors from two distributions P and Q are developed. Although an exact level α test can be constructed by a permutation test when the fundamental assumption of identical underlying distributions hold...

Mantel Permutation Tests