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A maximum likelihood method for latent class regression involving a censored dependent variable
dc.contributor.authorRamaswamy, Venkatramen_US
dc.contributor.authorJedidi, Kamelen_US
dc.contributor.authorDeSarbo, Wayne S.en_US
dc.date.accessioned2006-09-11T16:25:34Z
dc.date.available2006-09-11T16:25:34Z
dc.date.issued1993-09en_US
dc.identifier.citationJedidi, Kamel; Ramaswamy, Venkatram; Desarbo, Wayne S.; (1993). "A maximum likelihood method for latent class regression involving a censored dependent variable." Psychometrika 58(3): 375-394. <http://hdl.handle.net/2027.42/45751>en_US
dc.identifier.issn0033-3123en_US
dc.identifier.issn1860-0980en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/45751
dc.description.abstractThe standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.en_US
dc.format.extent1368846 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherSpringer-Verlag; Psychometric Societyen_US
dc.subject.otherStatistics for Social Science, Behavorial Science, Education, Public Policy, and Lawen_US
dc.subject.otherConsumer Psychologyen_US
dc.subject.otherPsychologyen_US
dc.subject.otherLatent Class Analysisen_US
dc.subject.otherStatistical Theory and Methodsen_US
dc.subject.otherPsychometricsen_US
dc.subject.otherAssessment, Testing and Evaluationen_US
dc.subject.otherCensored Regressionen_US
dc.subject.otherMaximum Likelihood Estimationen_US
dc.titleA maximum likelihood method for latent class regression involving a censored dependent variableen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPsychologyen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumMarketing and Statistics Departments School of Business Administration, University of Michigan, USAen_US
dc.contributor.affiliationumMarketing Department School of Business Administration, University of Michigan, USAen_US
dc.contributor.affiliationotherMarketing Department, Graduate School of Business, Columbia University, 10027, New York, NYen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/45751/1/11336_2005_Article_BF02294647.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1007/BF02294647en_US
dc.identifier.sourcePsychometrikaen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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