A Nonparametric Bayesian Approach to the Rare Type Match Problem

The “rare type match problem” is the situation in which, in a criminal case, the suspect’s DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has b...

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Main Authors:	Giulia Cereda, Richard D. Gill
Format:	Article
Language:	English
Published:	MDPI AG 2020-04-01
Series:	Entropy
Subjects:	forensic statistics likelihood ratio Bayesian nonparametric rare type match problem Y-STR
Online Access:	https://www.mdpi.com/1099-4300/22/4/439

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spelling	doaj-53808c103201407c9841943b756ea63d2020-11-25T02:27:11ZengMDPI AGEntropy1099-43002020-04-012243943910.3390/e22040439A Nonparametric Bayesian Approach to the Rare Type Match ProblemGiulia Cereda0Richard D. Gill1Mathematical Institute, Leiden University, Postbus 9512, 2300 RA Leiden, The NetherlandsMathematical Institute, Leiden University, Postbus 9512, 2300 RA Leiden, The NetherlandsThe “rare type match problem” is the situation in which, in a criminal case, the suspect’s DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person) should be based on the calculation of the likelihood ratio and depends on the population proportions of the DNA profiles that are unknown. We propose a Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet distribution as a prior over the ranked population proportions and discards the information about the names of the different DNA profiles. This model is validated using data coming from European Y-STR DNA profiles, and the calculation of the likelihood ratio becomes quite simple thanks to an Empirical Bayes approach for which we provided a motivation.https://www.mdpi.com/1099-4300/22/4/439forensic statisticslikelihood ratioBayesian nonparametricrare type match problemY-STR
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Giulia Cereda Richard D. Gill
spellingShingle	Giulia Cereda Richard D. Gill A Nonparametric Bayesian Approach to the Rare Type Match Problem Entropy forensic statistics likelihood ratio Bayesian nonparametric rare type match problem Y-STR
author_facet	Giulia Cereda Richard D. Gill
author_sort	Giulia Cereda
title	A Nonparametric Bayesian Approach to the Rare Type Match Problem
title_short	A Nonparametric Bayesian Approach to the Rare Type Match Problem
title_full	A Nonparametric Bayesian Approach to the Rare Type Match Problem
title_fullStr	A Nonparametric Bayesian Approach to the Rare Type Match Problem
title_full_unstemmed	A Nonparametric Bayesian Approach to the Rare Type Match Problem
title_sort	nonparametric bayesian approach to the rare type match problem
publisher	MDPI AG
series	Entropy
issn	1099-4300
publishDate	2020-04-01
description	The “rare type match problem” is the situation in which, in a criminal case, the suspect’s DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person) should be based on the calculation of the likelihood ratio and depends on the population proportions of the DNA profiles that are unknown. We propose a Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet distribution as a prior over the ranked population proportions and discards the information about the names of the different DNA profiles. This model is validated using data coming from European Y-STR DNA profiles, and the calculation of the likelihood ratio becomes quite simple thanks to an Empirical Bayes approach for which we provided a motivation.
topic	forensic statistics likelihood ratio Bayesian nonparametric rare type match problem Y-STR
url	https://www.mdpi.com/1099-4300/22/4/439
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A Nonparametric Bayesian Approach to the Rare Type Match Problem

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