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Segmented Bayesian calibration approach for estimating age in forensic science
Author(s) -
Bucci Andrea,
Skrami Edlira,
Faragalli Andrea,
Gesuita Rosaria,
Cameriere Roberto,
Carle Flavia,
Ferrante Luigi
Publication year - 2019
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900016
Subject(s) - bayesian probability , context (archaeology) , calibration , statistics , regression , estimation , computer science , econometrics , regression analysis , bayesian linear regression , data set , mathematics , artificial intelligence , bayesian inference , geography , engineering , archaeology , systems engineering
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.