Rank-Based Estimation for Arnold-Transformed Data

Rank-Based methods for linear models for independent and identically distributed data have been developed over the past 30 years. However, little work has been done in the area of mixed models. In this paper, we discuss a transformation approach to modeling a particular mixed model: one with an arbitrary number of fixed effects and covariates but only one random effect. Discussion of the asymptotic theory is given and the results of a simulation study verify the theory. These models are used to estimate the fixed effects from an experiment that uses a randomized block design.