Skeletal age-at-death estimation: Bayesian versus regression methods.

Age-at-death estimation in a skeletal assemblage (target sample) is biased by the demographic profile of the material used for age prediction (training sample) when this profile is different from that of the target sample. This bias is minimized if the demographic profile of the target sample is properly taken into account in the method developed for age-at-death estimation. In the Bayesian approach this is accomplished via the informative prior. For methods based on regression, we propose two techniques: (a) using weighting factors taken from the demographic profile of the target sample, and (b) creating a new hypothetical training sample that has a demographic profile similar to that of the target sample. The two techniques, as well as the Bayesian approach, were tested using 532 artificial systems in which the age marker exhibited an eight-grade expression. It was found that depending on the criteria used for evaluation, the proposed approaches and especially the one based on a hypothetical training sample, may give better results than the Bayesian method in more than 90% of the systems studied. A basic prerequisite for the good performance of the proposed approaches is to select carefully the training sample. This sample should exhibit a uniform demographic profile or a profile with almost equal numbers of young and older individuals. All the above hold if the training and the target samples have different demographic profiles. If the profiles are the same or very similar, the best aging method is the direct regression using simple linear models.