Segmented Bayesian calibration approach for estimating age in forensic science

• Published in: Biometrical journal Vol. 61; no. 6; pp. 1575 - 1594
• Main Authors: Bucci, Andrea, Skrami, Edlira, Faragalli, Andrea, Gesuita, Rosaria, Cameriere, Roberto, Carle, Flavia, Ferrante, Luigi
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.11.2019
• Subjects: Age; Age determination; age estimation; Age Factors; Bayes Theorem; Bayesian analysis; Bayesian calibration; Bias; Biometry - methods; Calibration; Chronology; Dentistry; Estimation; Forensic science; Forensic sciences; Forensic Sciences - methods; Growth rate; healthcare in young people; Humans; Juveniles; Mathematical models; Model accuracy; Models, Statistical; Normal distribution; Regression analysis; Regression models; segmented regression; Statistical analysis; Bayesian calibration; healthcare in young people; age estimation; segmented regression
• DOI: 10.1002/bimj.201900016
• ISSN: 0323-3847; 1521-4036

Forensic age estimation is receiving growing attention from researchers in the last few years. Accurate estimates of age are needed both for identifying real age in individuals without any identity document and assessing it for human remains. The methods applied in such context are mostly based on radiological analysis of some anatomical districts and entail the use of a regression model. However, estimating chronological age by regression models leads to overestimated ages in younger subjects and underestimated ages in older ones. We introduced a full Bayesian calibration method combined with a segmented function for age estimation that relied on a Normal distribution as a density model to mitigate this bias. In this way, we were also able to model the decreasing growth rate in juveniles. We compared our new Bayesian‐segmented model with other existing approaches. The proposed method helped producing more robust and precise forecasts of age than compared models while exhibited comparable accuracy in terms of forecasting measures. Our method seemed to overcome the estimation bias also when applied to a real data set of South‐African juvenile subjects.