Robust analysis of variance based upon a likelihood ratio criterion

Abstract

Robust tests of general linear hypotheses in linear models are developed. These are likelihood ratio type tests in the same sense that M -estimates are maximum likelihood type estimates. Construction of the tests suggests a decomposition of the data into terms analogous to classical sums of squares, providing a robust analysis of variance. Asymptotic efficiency and robustness properties of the tests are the same as those of the M -estimates upon which they are based.