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A Parametric Procedure for Ultrametric Tree Estimation from Conditional Rank Order Proximity Data

Published online by Cambridge University Press:  01 January 2025

Martin R. Young  and
Wayne S. DeSarbo
Martin R. Young*
Affiliation:
Statistics Department, School of Business Administration, University of Michigan
Wayne S. DeSarbo
Affiliation:
Marketing and Statistics Departments School of Business Administration, University of Michigan
*
Requests for reprints should be sent to Martin R. Young, Statistics Department, School of Business Administration, University of Michigan, Ann Arbor, MI 48109-1234.
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Abstract

The psychometric and classification literatures have illustrated the fact that a wide class of discrete or network models (e.g., hierarchical or ultrametric trees) for the analysis of ordinal proximity data are plagued by potential degenerate solutions if estimated using traditional nonmetric procedures (i.e., procedures which optimize a STRESS-based criteria of fit and whose solutions are invariant under a monotone transformation of the input data). This paper proposes a new parametric, maximum likelihood based procedure for estimating ultrametric trees for the analysis of conditional rank order proximity data. We present the technical aspects of the model and the estimation algorithm. Some preliminary Monte Carlo results are discussed. A consumer psychology application is provided examining the similarity of fifteen types of snack/breakfast items. Finally, some directions for future research are provided.

Keywords

hierarchical clusteringproximity dataconditional rank ordersmaximum likelihood estimationconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 60 , Issue 1 , March 1995 , pp. 47 - 75
Copyright
Copyright © 1995 The Psychometric Society

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