Highly resistant regression and object matching

In many disciplines, it is of great importance to match objects. Procrustes analysis is a popular method for comparing labeled point configurations ba sed on a least squares criterion. We consider alternative procedures that a re highly resistant to outlier points, and we describe an application in el ectrophoretic gel matching. We consider procedures based on S estimators, l east median of squares, and least quartile difference estimators. Practical implementation issues are discussed, including random subset selection and intelligent subset selection (where subsets with Small size or near collin ear subsets are ignored). The relative performances of the resistant and Pr ocrustes methods are examined in a simulation study.