Learning algorithms to evaluate forensic glass evidence

DOI10.1214/18-AOAS1211zbMath1423.62059OpenAlexW2950967107WikidataQ127664413 ScholiaQ127664413MaRDI QIDQ2318674

Alicia L. Carriquiry, So Young Park

Publication date: 15 August 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1560758438

zbMATH Keywords

random forestforensic glass comparisonsmultivariate measurementsout-of-bag errorsscore likelihood ratio

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (3)

An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust feature ⋮ Correction to: ``Statistical modeling and analysis of trace element concentrations in forensic Glass evidence ⋮ Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

Uses Software

• BayesTree
• bartMachine
• ROSE
• SMOTE
• BartPy

Cites Work

• Unnamed Item
• BART: Bayesian additive regression trees
• Evaluation of Trace Evidence in the Form of Multivariate Data
• Automatic matching of bullet land impressions
• Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
• The Statistical Interpretation of Forensic Glass Evidence
• Prediction with missing data via Bayesian Additive Regression Trees
• Combining Independent Tests of Significance
• Random forests

This page was built for publication: Learning algorithms to evaluate forensic glass evidence