Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity
dc.contributor.author | DeSarbo, Wayne S. | en_US |
dc.contributor.author | Chatterjee, Rabikar | en_US |
dc.contributor.author | Kim, Juyoung | en_US |
dc.date.accessioned | 2006-09-11T16:25:39Z | |
dc.date.available | 2006-09-11T16:25:39Z | |
dc.date.issued | 1994-12 | en_US |
dc.identifier.citation | DeSarbo, Wayne S.; Chatterjee, Rabikar; Kim, Juyoung; (1994). "Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity." Psychometrika 59(4): 527-566. <http://hdl.handle.net/2027.42/45752> | en_US |
dc.identifier.issn | 0033-3123 | en_US |
dc.identifier.issn | 1860-0980 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45752 | |
dc.description.abstract | This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective of the proposed TREEFAM procedure is to quantitatively “filter out” the effects of stimulus unfamiliarity in the estimation of an ultrametric tree. A conditional, alternating maximum likelihood procedure is formulated to simultaneously estimate an ultrametric tree, under the unobserved condition of complete stimulus familiarity, and subject-specific parameters capturing the adjustments due to differential unfamiliarity. We demonstrate the performance of the TREEFAM procedure under a variety of alternative conditions via a modest Monte Carlo experimental study. An empirical application provides evidence that the TREEFAM outperforms traditional models that ignore the effects of unfamiliarity in terms of superior tree recovery and overall goodness-of-fit. | en_US |
dc.format.extent | 2440755 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; Psychometric Society | en_US |
dc.subject.other | Maximum Likelihood Estimation | en_US |
dc.subject.other | Consumer Psychology | en_US |
dc.subject.other | Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law | en_US |
dc.subject.other | Psychology | en_US |
dc.subject.other | Psychometrics | en_US |
dc.subject.other | Statistical Theory and Methods | en_US |
dc.subject.other | Assessment, Testing and Evaluation | en_US |
dc.subject.other | Hierarchical Clustering | en_US |
dc.subject.other | Familiarity | en_US |
dc.title | Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Psychology | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | School of Business Administration, The University of Michigan, 48109-1234, Ann Arbor, MI | en_US |
dc.contributor.affiliationum | School of Business Administration, The University of Michigan, 48109-1234, Ann Arbor, MI | en_US |
dc.contributor.affiliationum | School of Business Administration, The University of Michigan, 48109-1234, Ann Arbor, MI | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45752/1/11336_2005_Article_BF02294391.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF02294391 | en_US |
dc.identifier.source | Psychometrika | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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