Single-Step Simple ROC Curve Fitting via PCA.
A simple approach to fitting curves to receiver operating characteristic rating data is presented. It is based on the first principal component of the covariance space of the inverse normal integral of the cumulative rating data of the targets and distractors. It provides for 2 new associated d' est...
| Publicado en: | Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale Vol. 70; no. 4; pp. 301 - 306 |
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| Formato: | Artículo |
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Canadian Psychological Association
Dec2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=119931277&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 119931277 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 11961961 CJX jtl: Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale issn: 11961961 maglogo: N pubinfo: dt: Dec2016 vid: 70 iid: 4 pid: 98 pub: Canadian Psychological Association artinfo: ui: 119931277 10.1037/cep0000095 ppf: 301 ppct: 5 formats: fmt: @attributes: type: P size: 435KB tig: atl: Single-Step Simple ROC Curve Fitting via PCA. aug: au: Vokey, John R. affil: University of Lethbridge su: Algorithms Factor analysis Programming languages Research funding Receiver operating characteristic curves sug: subj: Software Publishers Software publishers (except video game publishers) Algorithms Factor analysis Programming languages Research funding Receiver operating characteristic curves keyword: iterative maximum likelihood least squares PCA ROC curves signal detection theory courbes ROC méthode de vraisemblance maximale itérative moindres carrés théorie de détection de signal iterative maximum likelihood least squares PCA ROC curves signal detection theory courbes ROC méthode de vraisemblance maximale itérative moindres carrés théorie de détection de signal ab: A simple approach to fitting curves to receiver operating characteristic rating data is presented. It is based on the first principal component of the covariance space of the inverse normal integral of the cumulative rating data of the targets and distractors. It provides for 2 new associated d' estimates, d' and d'. A Monte Carlo simulation demonstrated that the parameter estimates are unbiased and produce estimates comparable to the iterative, maximum likelihood approach. The corresponding computational and plotting functions in the R programming language are also provided. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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