{"data":{"id":"10.6084/m9.figshare.c.2170502.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2170502.v1","prefix":"10.6084","suffix":"m9.figshare.c.2170502.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Yihuan Xu","affiliation":[],"nameIdentifiers":[]},{"name":"Iglewicz, Boris","nameType":"Personal","givenName":"Boris","familyName":"Iglewicz","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"False Discovery versus Familywise Error Rate Approaches to Outlier Detection"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences"},{"subject":"Chemistry"},{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Engineering"}],"contributors":[],"dates":[{"date":"2015-12-23","dateType":"Created"},{"date":"2015-12-23","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/19466315.2015.1119720","relatedIdentifierType":"DOI"},{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.c.2170502","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"ABSTRACTOutliers, in general, are observations that deviate sufficiently from a base distribution. This study deals with outlier detection approaches for large samples from continuous univariate distributions. Investigated are the properties of a practical newer outlier detection approach based on use of a false discovery rate method in conjunction with a robustly estimated Tukey g-and-h base distribution. Compared are the properties of a boxplot type outlier detection approach that controls the familywise error rate with a newer false discovery rate approach. These options are compared in terms of error rates and effects of moving the outliers gradually further from base distribution center while using 5% and 1% false discovery rate or familywise error rates. Two microarray data sets are used as examples where the assumed null distributions do not fit the data well. In such cases the proposed estimated Tukey g-and-h null distribution approach leads to superior outlier detection performance.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/collections/False_Discovery_versus_Familywise_Error_Rate_Approaches_to_Outlier_Detection/2170502/1","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-12-23T12:28:31.000Z","registered":"2015-12-23T12:28:32.000Z","published":"2015","updated":"2024-12-15T14:06:15.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","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