A latent class procedure for the structural analysis of two-way compositional data

Wayne S. DeSarbo, Venkatram Ramaswamy, Peter Lenk

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

This paper develops a new procedure for simultaneously performing multidimensional scaling and cluster analysis on two-way compositional data of proportions. The objective of the proposed procedure is to delineate patterns of variability in compositions across subjects by simultaneously clustering subjects into latent classes or groups and estimating a joint space of stimulus coordinates and class-specific vectors in a multidimensional space. We use a conditional mixture, maximum likelihood framework with an E-M algorithm for parameter estimation. The proposed procedure is illustrated using a compositional data set reflecting proportions of viewing time across television networks for an area sample of households.

Original languageEnglish (US)
Pages (from-to)159-193
Number of pages35
JournalJournal of Classification
Volume10
Issue number2
DOIs
StatePublished - Dec 1 1993

All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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