Segmented Bayesian calibration approach for estimating age in forensic science.
Bayesian calibration
age estimation
healthcare in young people
segmented regression
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
14
01
2019
revised:
04
07
2019
accepted:
11
07
2019
pubmed:
8
8
2019
medline:
22
5
2020
entrez:
8
8
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31389072
doi: 10.1002/bimj.201900016
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1575-1594Informations de copyright
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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