A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Meuwly, Didier; Ramos, Daniel; Haraksim, Rudolf

doi:10.1016/j.forsciint.2016.03.048

Cited by 124 publications

(116 citation statements)

References 41 publications

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…A single test set consisting of 1000 simulated data points from each category was generated for each condition, and likelihood ratios or Bayes factors were calculated for these test data using each training sample set and each of the four procedures. The root mean square (RMS) error between the calculated log likelihood ratio or log Bayes factors values and the log reference values was calculated, as was the log likelihood ratio cost (C llr , [6,15,[46][47][48]). Each metric can be considered a quantifier of a different form of accuracy.…”

Section: Exploration Of the Behaviour Of The Four Procedures Using Simentioning

confidence: 99%

See 1 more Smart Citation

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors

Morrison

Poh

2018

Science & Justice

View full text Add to dashboard Cite

A B S T R A C TWhen strength of forensic evidence is quantified using sample data and statistical models, a concern may be raised as to whether the output of a model overestimates the strength of evidence. This is particularly the case when the amount of sample data is small, and hence sampling variability is high. This concern is related to concern about precision. This paper describes, explores, and tests three procedures which shrink the value of the likelihood ratio or Bayes factor toward the neutral value of one. The procedures are: (1) a Bayesian procedure with uninformative priors, (2) use of empirical lower and upper bounds (ELUB), and (3) a novel form of regularized logistic regression. As a benchmark, they are compared with linear discriminant analysis, and in some instances with non-regularized logistic regression. The behaviours of the procedures are explored using Monte Carlo simulated data, and tested on real data from comparisons of voice recordings, face images, and glass fragments.

show abstract

Section: Exploration Of the Behaviour Of The Four Procedures Using Simentioning

confidence: 99%

“…The results of using the different procedures to convert the scores to likelihood ratios are shown as Tippett plots [48,52,53] in Fig. 11 (GMM-UBM scores) and Fig.…”

Section: Forensic Comparison Of Voice Recordings: Scores From Gmm-ubmmentioning

confidence: 99%

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors

Morrison

Poh

2018

Science & Justice

View full text Add to dashboard Cite

show abstract

“…These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim,) [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1].…”

mentioning

confidence: 99%

“…The data serve for reproducibility of validation reports of automatic forensic evaluation methods as described in [1].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

2017

Self Cite

View full text Add to dashboard Cite

Data to which the authors refer to throughout this article are likelihood ratios (LR) computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR) method in forensic evidence evaluation. These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim,) [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1]. Alongside the data, a justification and motivation for the use of methods is given. These methods calculate LRs from the fingerprint/mark data and are subject to a validation procedure. The choice of using real forensic fingerprint in the validation and simulated data in the development is described and justified. Validation criteria are set for the purpose of validation of the LR methods, which are used to calculate the LR values from the data and the validation report. For privacy and data protection reasons, the original fingerprint/mark images cannot be shared. But these images do not constitute the core data for the validation, contrarily to the LRs that are shared.

show abstract

References

2020

Statistics and the Evaluation of Evidence for Forensic Scientists 3e

View full text Add to dashboard Cite

A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Cited by 124 publications

References 41 publications

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors

Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors

Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

References

Contact Info

Product

Resources

About