Comparison of Statistical Models for the Estimation of Age at Death Using Subjective Adult Human Dental Indicators

Isaac Dialsingh,Shalini Pooransingh

doi:10.4172/2157-7145.1000340

Abstract

Objective: The main objective of this paper is the estimation of age at death using subjective dental data. This is particularly useful in developing and under developed countries. Methods: This study provides a framework for the estimation of age at death using very subjective measurements of the teeth using (i) Generalized Linear Models (GLMs) and (ii) Generalized Additive Models (GAMs). These predictors of age were all ordinal in nature. A dataset comprising measurements taken on 71 maxillary incisors from different individuals at the time of their death was used. A comparison of two models – the Gamma GLM and the Gamma GAM is used to illustrate the flexibility of this method and the predictive power of the statistical modelling process. Results: The study showed the effectiveness of the models through the Akaike Information Criterion (AIC) as well as the proportion of correct predictions within each of the age groups. The Gamma GAM actually had the higher AIC but the better predictive values within the age groups. Conclusion: Statistical modelling caters for the types of data and can give reasonable predictions of age at death.

Highlights

In developed and developing countries, because of a lack of financial resources, it is often the case that law enforcement and forensic officers are faced with the daunting task of estimating age at death when confronted with partially decomposed human remains
Past studies used mainly linear regression techniques in age estimation and relied on observational measurements taken on the teeth [2,3,4,5]
This study shows that with sufficient data and simple subjective measurements on the teeth of human remains it is possible to predict the age of death of a person within reasonable bounds

Summary

Introduction

In developed and developing countries, because of a lack of financial resources, it is often the case that law enforcement and forensic officers are faced with the daunting task of estimating age at death when confronted with partially decomposed human remains. This paper illustrates the efficacy of the GLM and GAM as alternative models for estimation of age at death. It is an extension of work done on a paper [6] on the same dataset. These methods are often more flexible than linear regression especially when the distribution of the response variable (in our case age) is not normally distributed. To the best of our knowledge, this is the first time the GAM is being used in forensic science applied to dental variables

Methods

Results

Discussion

Conclusion