Univariate and Linear Composite Asymmetry Statistics for the "Pair-Matching" of Bone Antimeres.

This paper examines the distributional properties of univariate and linear composite measures of long bone asymmetry. The goal of this paper is to examine models that best fit the distribution of asymmetries with implications for the improvement of forensic pair-matching techniques. We use the software R to model reference data (N = 2343) and test data (N = 71) as normal distributions, an exponential power distribution, and a skew exponential power distribution-the latter two include the normal as a special case. Our results indicate that the data best fit the latter two distributions because the data are nonnormal. We also show how asymmetry statistics that use absolute values of side differences can be fit as folded distributions. This obviates the need for empirical distributions or for transformations that attempt to convert nonnormal distributions to normal distributions. The results of this study lay the framework for improving pair-matching methods that use comparative reference data.