A maximum likelihood methodology for clusterwise linear regression
dc.contributor.author | DeSarbo, Wayne S. | en_US |
dc.contributor.author | Cron, William L. | en_US |
dc.date.accessioned | 2006-09-11T17:10:32Z | |
dc.date.available | 2006-09-11T17:10:32Z | |
dc.date.issued | 1988-09 | en_US |
dc.identifier.citation | DeSarbo, Wayne S.; Cron, William L.; (1988). "A maximum likelihood methodology for clusterwise linear regression." Journal of Classification 5(2): 249-282. <http://hdl.handle.net/2027.42/45937> | en_US |
dc.identifier.issn | 1432-1343 | en_US |
dc.identifier.issn | 0176-4268 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45937 | |
dc.description.abstract | This paper presents a conditional mixture, maximum likelihood methodology for performing clusterwise linear regression. This new methodology simultaneously estimates separate regression functions and membership in K clusters or groups. A review of related procedures is discussed with an associated critique. The conditional mixture, maximum likelihood methodology is introduced together with the E-M algorithm utilized for parameter estimation. A Monte Carlo analysis is performed via a fractional factorial design to examine the performance of the procedure. Next, a marketing application is presented concerning the evaluations of trade show performance by senior marketing executives. Finally, other potential applications and directions for future research are identified. | en_US |
dc.format.extent | 2100600 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag; Springer-Verlag New York Inc. | en_US |
dc.subject.other | Multiple Regression | en_US |
dc.subject.other | E-M Algorithm | en_US |
dc.subject.other | Statistics | en_US |
dc.subject.other | Statistics, General | en_US |
dc.subject.other | Cluster Analysis | en_US |
dc.subject.other | Maximum Likelihood Estimation | en_US |
dc.subject.other | Marketing Trade Shows | en_US |
dc.title | A maximum likelihood methodology for clusterwise linear regression | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Departments of Marketing and Statistics, Business School of the University of Michigan, 48104, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationother | Department of Marketing, Edwin L. Cox School of Business, Southern Methodist University, 75275, Dallas, TX, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45937/1/357_2005_Article_BF01897167.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF01897167 | en_US |
dc.identifier.source | Journal of Classification | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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