Estimation of parameters in DNA mixture analysis

Therese Graversen; Steffen Lauritzen

doi:10.1080/02664763.2013.817549

Estimation of parameters in DNA mixture analysis

4 Citationer (Scopus)

Abstract

In [7], a Bayesian network for analysis of mixed traces of DNA was presented using gamma distributions for modelling peak sizes in the electropherogram. It was demonstrated that the analysis was sensitive to the choice of a variance factor and hence this should be adapted to any new trace analysed. In this paper, we discuss how the variance parameter can be estimated by maximum likelihood to achieve this. The unknown proportions of DNA from each contributor can similarly be estimated by maximum likelihood jointly with the variance parameter. Furthermore, we discuss how to incorporate prior knowledge about the parameters in a Bayesian analysis. The proposed estimation methods are illustrated through a few examples of applications for calculating evidential value in casework and for mixture deconvolution.

Originalsprog	Engelsk
Tidsskrift	Journal of Applied Statistics
Vol/bind	40
Udgave nummer	11
Sider (fra-til)	2423-2436
Antal sider	14
ISSN	0266-4763
DOI	https://doi.org/10.1080/02664763.2013.817549
Status	Udgivet - nov. 2013
Udgivet eksternt	Ja

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10.1080/02664763.2013.817549

Citationsformater

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abstract = "In [7], a Bayesian network for analysis of mixed traces of DNA was presented using gamma distributions for modelling peak sizes in the electropherogram. It was demonstrated that the analysis was sensitive to the choice of a variance factor and hence this should be adapted to any new trace analysed. In this paper, we discuss how the variance parameter can be estimated by maximum likelihood to achieve this. The unknown proportions of DNA from each contributor can similarly be estimated by maximum likelihood jointly with the variance parameter. Furthermore, we discuss how to incorporate prior knowledge about the parameters in a Bayesian analysis. The proposed estimation methods are illustrated through a few examples of applications for calculating evidential value in casework and for mixture deconvolution.",

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AU - Graversen, Therese

AU - Lauritzen, Steffen

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AB - In [7], a Bayesian network for analysis of mixed traces of DNA was presented using gamma distributions for modelling peak sizes in the electropherogram. It was demonstrated that the analysis was sensitive to the choice of a variance factor and hence this should be adapted to any new trace analysed. In this paper, we discuss how the variance parameter can be estimated by maximum likelihood to achieve this. The unknown proportions of DNA from each contributor can similarly be estimated by maximum likelihood jointly with the variance parameter. Furthermore, we discuss how to incorporate prior knowledge about the parameters in a Bayesian analysis. The proposed estimation methods are illustrated through a few examples of applications for calculating evidential value in casework and for mixture deconvolution.

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DO - 10.1080/02664763.2013.817549

M3 - Journal article

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EP - 2436

JO - Journal of Applied Statistics

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Estimation of parameters in DNA mixture analysis

Abstract

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