An Evaluation Of Error Confidence Interval Estimation Methods
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Estimation Methods Pattern Recognition, International Conference on (2004) Cambridge UK Aug. 23, 2004 to Aug. 26, 2004 ISSN: 1051-4651 ISBN: 0-7695-2128-2 pp: 103-106 DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334479 Sharath Pankanti , IBM standard error of estimate confidence interval Thomas J. Watson Research Center, Yorktown Heights, NY Nalini K. Ratha , IBM
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Thomas J. Watson Research Center, Yorktown Heights, NY Ruud M. Bolle , IBM Thomas J. Watson Research Center, Yorktown
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Heights, NY ABSTRACT Reporting the accuracy performance of pattern recognition systems (e.g., biometrics ID system) is a controversial issue and perhaps an issue that is not well understood [An introduction to evaluating biometric
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systems, Confidence interval and test size estimation for biometric data]. This work focuses on the research issues related to the oft used confidence interval metric for performance evaluation. Using a biometric (fingerprint) authentication system, we estimate the False Reject Rates and False Accept Rates of the system using a real fingerprint dataset. We also estimate confidence intervals of these error rates using a number of parametric confidence interval estimation and prediction interval excel (e.g., see [Confidence interval and test size estimation for biometric data]) and non-parametric (e.g., bootstrapping [1, 3, 6]) methods. We attempt to assess the accuracy of the confidence intervals based on estimate and verify strategy applied to repetitive random train/test splits of the dataset. Our experiments objectively verify the hypothesis that the traditional bootstrap and parametric estimate methods are not very effective in estimating the confidence intervals and magnitude of interdependence among data may be one of the reasons for their ineffective estimates. Further, we demonstrate that the resampling the subsets of the data samples (inspired from moving block bootstrap [Moving blocks Jackknife and Bootstrap capture weak dependence]) may be one way of replicating interdependence among the data; the bootstrapping methods using such subset resampling may indeed improve the accuracy of the estimates. Irrespective of the method of estimation, the results show that the (1 - α) 100% confidence intervals empirically estimated from the training set capture significantly smaller than (1 - α) fraction of the estimates obtained from the test set. INDEX TERMS null CITATION Sharath Pankanti, Nalini K. Ratha, Ruud M. Bolle, "An Evaluation of Error Confidence Interval Estimation Methods", Pattern Recognition, Interna
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI confidence interval estimation of interaction Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch confidence interval estimation of a normal percentile termSearch Advanced Journal list Help Journal ListHHS Author ManuscriptsPMC3661219 Eur J Clin confidence interval estimation pdf Microbiol Infect Dis. Author manuscript; available in PMC 2013 May 22.Published in final edited form as:Eur J Clin Microbiol Infect http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334479 Dis. 2012 Sep; 31(9): 2111–2116. Published online 2012 Mar 29. doi: 10.1007/s10096-012-1602-1PMCID: PMC3661219NIHMSID: NIHMS458770Methods and recommendations for evaluating and reporting a new diagnostic testA. S. Hess, M. Shardell, J. K. Johnson, K. A. Thom, P. Strassle, G. Netzer, and http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3661219/ A. D. HarrisUniversity of Maryland School of Medicine, Baltimore, MD, USACorresponding author.A. S. Hess: ude.dnalyramu.ipe@sseha Author information ► Copyright and License information ►Copyright notice and DisclaimerThe publisher's final edited version of this article is available at Eur J Clin Microbiol Infect DisSee other articles in PMC that cite the published article.AbstractNo standardized guidelines exist for the biostatistical methods appropriate for studies evaluating diagnostic tests. Publication recommendations such as the STARD statement provide guidance for the analysis of data, but biostatistical advice is minimal and application is inconsistent. This article aims to provide a self-contained, accessible resource on the biostatistical aspects of study design and reporting for investigators. For all dichotomous diagnostic tests, estimates of sensitivity and s
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