Permutation Predictor Tests in Linear Regression

Permutation Tests for Linear Models

Several approximate permutation tests have been proposed for tests of partial regression coefficients in a linear model based on sample partial correlations. This paper begins with an explanation and notation for an exact test. It then compares the distributions of the test statistics under the various permutation methods proposed, and shows that the partial correlations under permutation are a...

Permutation tests for linear models in R

An R package which uses permutation tests to obtain p-values for linear models. Standard R linear model functions have been modified to produce p-values obtained from permutation tests instead of from normal theory. These values are advantageous when the degrees of freedom for error is small or non-existent, as is the case with saturated experimental designs, or when the data is drawn from a no...

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.

Bootstrap Tests of Nonnested Linear Regression

Permutation Tests in Clinical Trials

Obviously, the more complex the statistical analysis, the more assumptions are involved. In a thorough statistical analysis, efforts are made to check the validity of the assumptions and to examine the sensitivity of the results to departures from the assumptions. In routine analyses, however, these checks are often skipped. Moreover, and more critically, the checks are not foolproof, and even ...