Performance of three mathematical models for estimating age-at-death from multiple indicators of the adult skeleton

The mathematical method which will achieve the most accurate and precise age-at-death estimate from the adult skeleton is often debated. Some research promotes Bayesian analysis, which is widely considered better suited to the data construct of adult age-at-death distributions. Other research indicates that methods with less mathematical complexity produce equally accurate and precise age-at-death estimates. One of the advantages of Bayesian analysis is the ability to systematically combine multiple indicators, which is reported to improve the age-at-death estimate. Few comparisons exist between Bayesian analysis and less complex mathematical models when considering multiple skeletal indicators. This study aims to evaluate the performance of a Bayesian approach compared to a phase-based averaging method and linear regression analysis using multiple skeletal indicators. The three combination methods were constructed from age-at-death data collected from 330 adult skeletons contained in the Raymond A Dart and Pretoria Bone Collections in South Africa. These methods were tested and compared using a hold-out sample of 30 skeletons. As is frequently reported in literature, a balance between accuracy and precision was difficult to obtain from the three selected methods. However, the averaging and regression analysis methods outperformed the Bayesian approach in both accuracy and precision. Nevertheless, each method may be suited to its own unique situation-averaging to inform first impressions, multiple linear regression to achieve statistically defensible accuracies and precisions and Bayesian analysis to allow for cases where category adjustments or missing indicators are necessary.

Automated summary

The choice of mathematical methods is crucial for accurate and precise age-at-death estimation from adult skeletons. Bayesian analysis is effective in combining multiple indicators for improved estimates, but may be less accurate and precise compared to simpler averaging and regression analysis methods. Each method has its own strengths – averaging for initial impressions, linear regression for statistically defensible accuracy and precision, and Bayesian analysis for cases requiring category adjustments or missing indicators.