Robust nonparametrics in mixed-MANOCOVA models

Abstract

In robust and nonparametric MANOVA models, the basic assumptions of independence, homoscedasticity and multinormality of the error components have been relaxed to a certain extent. In mixed-effects MANOVA models, the random effects components (due to concomitant variates) rest on the linearity of the regression function and some other distributional homogeneity conditions that may not hold universally, and avoidance of such regularity conditions generally introduce complications. Some of these difficulties are eliminated here through a conditional functional estimation approach, and in this setup, improved estimation of the fixed effects parameters is presented in a unified manner. Robustness and efficacy of these nonparametric procedures are appraised, and the picture is compared with their parametric as well as semiparametric counterparts.