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

✍ Scribed by Suzanne Winsberg; Geert De Soete

Publisher
John Wiley and Sons
Year
2002
Tongue
English
Weight
167 KB
Volume
18
Category
Article
ISSN
1524-1904

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✦ Synopsis

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 Β© 2002 John Wiley & Sons, Ltd.

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