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A bootstrap procedure for mixture models: applied to multidimensional scaling latent class models

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  • Suzanne Winsberg
  • Geert De Soete

Abstract

A bootstrap procedure useful in latent class, or more general mixture models has been developed to determine the sufficient number of latent classes or components 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. Also presented is a bootstrap technique for determining standard errors for estimates of the stimulus co‐ordinates, parameters of the multidimensional scaling model. Real and artificial 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 (J. Roy. Statist. Soc. Ser B 1968; 30: 582) procedure. The bootstrap procedures to estimate parameter stability appear to correctly re‐produce Monte Carlo results. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Suzanne Winsberg & Geert De Soete, 2002. "A bootstrap procedure for mixture models: applied to multidimensional scaling latent class models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(4), pages 391-406, October.
    • Handle: RePEc:wly:apsmbi:v:18:y:2002:i:4:p:391-406
    DOI: 10.1002/asmb.467
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    Cited by:

    1. Tomoya Okubo & Shin-ichi Mayekawa, 2015. "Modeling Viewpoint Shifts in Probabilistic Choice," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 412-427, June.

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