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dc.contributor.authorZhang, Hepingen_US
dc.date.accessioned2009-01-29T14:54:21Z-
dc.date.available2009-01-29T14:54:21Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22203-
dc.description.abstractTree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. The applicationsof these methods are far reaching. They include financial firms (credit cards: Altman, 2002; Frydman et al., 2002, and investments: Pace, 1995; Brennan et al., 2001), manufacturing and marketing companies (Levin et al., 1995), and pharmaceutical companies. The best documented, and arguably most popular uses of tree-based methods are in biomedical research for which classification is a central issue. For example, a clinician or health scientist may be very interested in the following question (Goldman et al., 1996, 1982; Zhang et al., 2001): Is this patient with chest pain suffering a heart attack, or does he simply have a strained muscle? To answer this question, information on this patient must be collected, and a good diagnostic test utilizing such information must be in place. Tree-based methods provide one solution for constructing the diagnostic test.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,30en_US
dc.subject.ddc330en_US
dc.titleRecursive Partitioning and Tree-based Methodsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495308420en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200430-
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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