A comment on the PCAST report: Skip the “match”/“non-match” stage

Highlights • Feature-comparison methods produce continuously-valued data. • The PCAST report advocates a two-stage procedure: • (1) Dichotomise the data into “match” or “non-match”. • (2) If “match”, assess correct acceptance and false acceptance rates. • A better procedure would directly statistica...

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Veröffentlicht in:Forensic science international 2017-03, Vol.272, p.e7-e9
Hauptverfasser: Morrison, Geoffrey Stewart, Kaye, David H, Balding, David J, Taylor, Duncan, Dawid, Philip, Aitken, Colin G.G, Gittelson, Simone, Zadora, Grzegorz, Robertson, Bernard, Willis, Sheila, Pope, Susan, Neil, Martin, Martire, Kristy A, Hepler, Amanda, Gill, Richard D, Jamieson, Allan, Zoete, Jacob de, Ostrum, R. Brent, Caliebe, Amke
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container_end_page e9
container_issue
container_start_page e7
container_title Forensic science international
container_volume 272
creator Morrison, Geoffrey Stewart
Kaye, David H
Balding, David J
Taylor, Duncan
Dawid, Philip
Aitken, Colin G.G
Gittelson, Simone
Zadora, Grzegorz
Robertson, Bernard
Willis, Sheila
Pope, Susan
Neil, Martin
Martire, Kristy A
Hepler, Amanda
Gill, Richard D
Jamieson, Allan
Zoete, Jacob de
Ostrum, R. Brent
Caliebe, Amke
description Highlights • Feature-comparison methods produce continuously-valued data. • The PCAST report advocates a two-stage procedure: • (1) Dichotomise the data into “match” or “non-match”. • (2) If “match”, assess correct acceptance and false acceptance rates. • A better procedure would directly statistically model the continuously-valued data.
doi_str_mv 10.1016/j.forsciint.2016.10.018
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source Elsevier ScienceDirect Journals
subjects False alarm
Forensic sciences
Forensic statistics
Likelihood ratio
Match/non-match
Methods
Pathology
PCAST report
Sensitivity
Validity
title A comment on the PCAST report: Skip the “match”/“non-match” stage
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