Rank-based analysis of repeated measures block designs

Abstract

A rank-based inference is developed for repeated measures balanced incomplete block and randomized complete block designs using a suitable dispersion function. Asymptotic distributions of rank estimators are developed after establishing approximate linearity of the gradient vector of the dispersion function. Unlike available nonparametric procedures for those designs, estimation and testing are tied together. Three different test statistics are developed for testing the linear hypotheses. Friedman's (1937) statistic and Durbin's (1951) statistic are particular cases of one of the three proposed statistics. An estimate of a scale parameter which appears in the ARE expression as well as as in the variences and covariances of the rank estimators is discussed.

Keywords:

• dispersion function
• rank estimates
• repeated measures
• gradient vector
• approximate linearity

Additional information

Notes on contributors

M. Mushfiqur Rashid

James C. Aubuchon