Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity

Wayne S. DeSarbo, Rabikar Chatterjee, Juyoung Kim

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)527-566
Number of pages40
JournalPsychometrika
Volume59
Issue number4
DOIs
StatePublished - Dec 1994

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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