Bootstrap methods for measuring classification uncertainty in latent class analysis

José G. Dias () and Jeroen K. Vermunt ()
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José G. Dias: Edifício ISCTE, ISCTE — Higher Institute of Social Sciences and Business Studies
Jeroen K. Vermunt: Tilburg University, Department of Methodology and Statistics

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 31-41 from Springer

Abstract: Abstract This paper addresses the issue of classification uncertainty in latent class analysis. It proposes a new bootstrap-based approach for quantifying the level of classification uncertainty at both the individual and the aggregate level. The procedure is illustrated by means of two applications.

Keywords: Latent class model; classification uncertainty; bootstrap estimation; model-based clustering; finite mixture models (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_3

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DOI: 10.1007/978-3-7908-1709-6_3

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