Abstract
This paper deals with improved measures of statistical accuracy for parameter estimates of latent class models. It introduces more precise confidence intervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced. The results are illustrated using a well-known dataset.
His research was supported by Fundação para a Ciência e Tecnologia Grant no. SFRH/BD/890/2000 (Portugal) and conducted at the University of Groningen (Population Research Centre and Faculty of Economics), The Netherlands. I would like to thank Jeroen Vermunt and one referee for their helpful comments on a previous draft of the manuscript.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Dias, J.G. (2005). Bootstrapping Latent Class Models. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_11
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DOI: https://doi.org/10.1007/3-540-28084-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
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