Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669754
Title: Statistical and computational methodology for the analysis of forensic DNA mixtures with artefacts
Author: Graversen, Therese
ISNI:       0000 0004 5369 4624
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Abstract:
This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.
Supervisor: Lauritzen, Steffen L. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.669754  DOI: Not available
Keywords: Statistics (see also social sciences) ; Computationally-intensive statistics ; Mathematical genetics and bioinformatics (statistics) ; Statistics (social sciences) ; Computing ; DNA mixture analysis ; Bayesian network ; statistics ; forensic genetics ; probability propagation ; gamma distribution ; probabilistic expert system
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