Multivariate multiple regression analyses: a permutation method for linear models
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Updated: Sep 29, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Paul W Mielke1, Kenneth J Berry
1Department of Statistics, Colorado State University, Fort Collins 80523-1877, USA. miekke@lamar.colostate.edu
Abstract:
A multivariate extension of a univariate procedure for the analysis of experimental designs is presented. A Euclidean-distance permutation procedure is used to evaluate multivariate residuals obtained from a regression algorithm, also based on Euclidean distances. Applications include various completely randomized and randomized block experimental designs such as one-way, Latin square, factorial, nested, and split-plot designs, with and without covariates. Unlike parametric procedures, the only required assumption is the randomization of subjects to treatments.
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