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A two-level model for evidence evaluation.

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A two-level model for evidence evaluation. / Lucy, David; Aitken, C. G. G.; Zadora, G.
In: Journal of Forensic Sciences, Vol. 52, No. 2, 03.2007, p. 412-419.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lucy, D, Aitken, CGG & Zadora, G 2007, 'A two-level model for evidence evaluation.', Journal of Forensic Sciences, vol. 52, no. 2, pp. 412-419. https://doi.org/10.1111/j.1556-4029.2006.00358.x

APA

Lucy, D., Aitken, C. G. G., & Zadora, G. (2007). A two-level model for evidence evaluation. Journal of Forensic Sciences, 52(2), 412-419. https://doi.org/10.1111/j.1556-4029.2006.00358.x

Vancouver

Lucy D, Aitken CGG, Zadora G. A two-level model for evidence evaluation. Journal of Forensic Sciences. 2007 Mar;52(2):412-419. doi: 10.1111/j.1556-4029.2006.00358.x

Author

Lucy, David ; Aitken, C. G. G. ; Zadora, G. / A two-level model for evidence evaluation. In: Journal of Forensic Sciences. 2007 ; Vol. 52, No. 2. pp. 412-419.

Bibtex

@article{611fe201eb6c4c7eb6232910a60546e9,
title = "A two-level model for evidence evaluation.",
abstract = "A random effects model using two levels of hierarchical nesting has been applied to the calculation of a likelihood ratio as a solution to the problem of comparison between two sets of replicated multivariate continuous observations where it is unknown whether the sets of measurements shared a common origin. Replicate measurements from a population of such measurements allow the calculation of both within-group and between-group variances/covariances. The within-group distribution has been modelled assuming a Normal distribution, and the between-group distribution has been modelled using a kernel density estimation procedure. A graphical method of estimating the dependency structure among the variables has been used to reduce this highly multivariate problem to several problems of lower dimension. The approach was tested using a database comprising measurements of eight major elements from each of four fragments from each of 200 glass objects and found to perform well compared with previous approaches, achieving a 15.2% false-positive rate, and a 5.5% false-negative rate. The modelling was then applied to two examples of casework in which glass found at the scene of the criminal activity has been compared with that found in association with a suspect.",
author = "David Lucy and Aitken, {C. G. G.} and G. Zadora",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2007",
month = mar,
doi = "10.1111/j.1556-4029.2006.00358.x",
language = "English",
volume = "52",
pages = "412--419",
journal = "Journal of Forensic Sciences",
issn = "0022-1198",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - A two-level model for evidence evaluation.

AU - Lucy, David

AU - Aitken, C. G. G.

AU - Zadora, G.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2007/3

Y1 - 2007/3

N2 - A random effects model using two levels of hierarchical nesting has been applied to the calculation of a likelihood ratio as a solution to the problem of comparison between two sets of replicated multivariate continuous observations where it is unknown whether the sets of measurements shared a common origin. Replicate measurements from a population of such measurements allow the calculation of both within-group and between-group variances/covariances. The within-group distribution has been modelled assuming a Normal distribution, and the between-group distribution has been modelled using a kernel density estimation procedure. A graphical method of estimating the dependency structure among the variables has been used to reduce this highly multivariate problem to several problems of lower dimension. The approach was tested using a database comprising measurements of eight major elements from each of four fragments from each of 200 glass objects and found to perform well compared with previous approaches, achieving a 15.2% false-positive rate, and a 5.5% false-negative rate. The modelling was then applied to two examples of casework in which glass found at the scene of the criminal activity has been compared with that found in association with a suspect.

AB - A random effects model using two levels of hierarchical nesting has been applied to the calculation of a likelihood ratio as a solution to the problem of comparison between two sets of replicated multivariate continuous observations where it is unknown whether the sets of measurements shared a common origin. Replicate measurements from a population of such measurements allow the calculation of both within-group and between-group variances/covariances. The within-group distribution has been modelled assuming a Normal distribution, and the between-group distribution has been modelled using a kernel density estimation procedure. A graphical method of estimating the dependency structure among the variables has been used to reduce this highly multivariate problem to several problems of lower dimension. The approach was tested using a database comprising measurements of eight major elements from each of four fragments from each of 200 glass objects and found to perform well compared with previous approaches, achieving a 15.2% false-positive rate, and a 5.5% false-negative rate. The modelling was then applied to two examples of casework in which glass found at the scene of the criminal activity has been compared with that found in association with a suspect.

U2 - 10.1111/j.1556-4029.2006.00358.x

DO - 10.1111/j.1556-4029.2006.00358.x

M3 - Journal article

VL - 52

SP - 412

EP - 419

JO - Journal of Forensic Sciences

JF - Journal of Forensic Sciences

SN - 0022-1198

IS - 2

ER -