{"data":{"id":"10.6084/m9.figshare.1407389","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1407389","prefix":"10.6084","suffix":"m9.figshare.1407389","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Fouskakis, Dimitris","nameType":"Personal","givenName":"Dimitris","familyName":"Fouskakis","affiliation":[],"nameIdentifiers":[]},{"name":"Ntzoufras, Ioannis","nameType":"Personal","givenName":"Ioannis","familyName":"Ntzoufras","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Power-Conditional-Expected Priors: Using \u003ci\u003eg\u003c/i\u003e-Priors With Random Imaginary Data for Variable Selection"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2016,"subjects":[{"subject":"Biochemistry"},{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Physiology"},{"subject":"Pharmacology"},{"subject":"Biotechnology"},{"subject":"Evolutionary Biology"},{"subject":"Ecology"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"110309 Infectious Diseases","subjectScheme":"FOR"},{"subject":"FOS: Health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Health sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-05-07","dateType":"Created"},{"date":"2016-08-05","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/10618600.2015.1036996","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1792281 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The Zellner's \u003ci\u003eg\u003c/i\u003e-prior and its recent hierarchical extensions are the most popular default prior choices in the Bayesian variable selection context. These prior setups can be expressed as power-priors with fixed set of imaginary data. In this article, we borrow ideas from the power-expected-posterior (PEP) priors to introduce, under the \u003ci\u003eg\u003c/i\u003e-prior approach, an extra hierarchical level that accounts for the imaginary data uncertainty. For normal regression variable selection problems, the resulting power-conditional-expected-posterior (PCEP) prior is a conjugate normal-inverse gamma prior that provides a consistent variable selection procedure and gives support to more parsimonious models than the ones supported using the \u003ci\u003eg\u003c/i\u003e-prior and the hyper-\u003ci\u003eg\u003c/i\u003e prior for finite samples. Detailed illustrations and comparisons of the variable selection procedures using the proposed method, the \u003ci\u003eg\u003c/i\u003e-prior, and the hyper-\u003ci\u003eg\u003c/i\u003e prior are provided using both simulated and real data examples. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/articles/dataset/Power_Conditional_Expected_Priors_Using_i_g_i_priors_with_Random_Imaginary_Data_for_Variable_Selection/1407389/1","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","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":"2015-05-07T20:40:05.000Z","registered":"2015-05-07T20:40:06.000Z","published":"2016","updated":"2020-09-04T17:39:2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