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A Bootstrap Procedure for Mixture Models

  • Suzanne Winsberg
  • Geert deSoete
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

A bootstrap procedure useful in latent class models has been developed to determine the sufficient number of latent classes required to account for systematic group differences in the data. The procedure is illustrated in the context of a multidimensional scaling latent class model, CLASCAL. Real and artficial data are presented. The bootstrap procedure for selecting a sufficient number of classes seems to correctly select the correct number of latent classes at both low and high error levels. At higher error levels it outperforms Hope’s (1968) procedure.

Keywords

Latent Classis Bootstrap Sample Bootstrap Procedure Latent Class Model Artificial Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. DE SOETE, G. and DE SARBO, W. (1991). A latent class Probit model for analyzing pick any/N data, Journal of Classification, 8, 45–63.CrossRefGoogle Scholar
  2. EFRON, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistic, 7, 1–26.CrossRefGoogle Scholar
  3. EFRON, B. and TIBSHIRANI, R. (1993). An Introduction to the Bootstrap Chapman and Hall, New York.Google Scholar
  4. HOPE, A.C. (1968). A simplified Monte Carlo test procedure. Journal of the Royal Statistical Society, Series B 30, 582–598.Google Scholar
  5. WINSBERG, S. and DE SOETE, G. (1993). A latent class approach to fitting the weighted Euclidean model, CLASCAL. Psychometrika, 58, 315–330.CrossRefGoogle Scholar
  6. WINSBERG, S. and DE SOETE, G. (1997). Multidimensional scaling with constrained dimensions: CONSCAL. British Journal of Mathematical and Statistical Psychology, 50, 55–72CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • Suzanne Winsberg
    • 1
  • Geert deSoete
    • 2
  1. 1.IRCAMParisFrance
  2. 2.ARC, University of GhentGhentBelgium

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