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Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm

Author

Listed:
  • Peter G. M. van der Heijden
  • Jos Dessens
  • UIf Bockenholt

Abstract

Latent class analysis assumes the existence of a categorical latent variable that explains the relations between a set of categorical manifest variables. Simultaneous latent class analysis deals with sets of multiway contingency tables simultaneously. In this way an explanatory categorical grouping variable is related to latent class results. In this article we discuss a tool called the concomitant-variable latent-class model, which generalizes this work to continuous explanatory variables. An EM estimation procedure to estimate the model is worked out in detail, and the model is applied to an example on juvenile delinquency.

Suggested Citation

  • Peter G. M. van der Heijden & Jos Dessens & UIf Bockenholt, 1996. "Estimating the Concomitant-Variable Latent-Class Model With the EM Algorithm," Journal of Educational and Behavioral Statistics, , vol. 21(3), pages 215-229, September.
    • Handle: RePEc:sae:jedbes:v:21:y:1996:i:3:p:215-229
    DOI: 10.3102/10769986021003215
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    Cited by:

    1. Durante, Daniele & Canale, Antonio & Rigon, Tommaso, 2019. "A nested expectation–maximization algorithm for latent class models with covariates," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 97-103.
    2. Marco Centoni & Vieri Del Panta & Antonello Maruotti & Valentina Raponi, 2019. "Concomitant-Variable Latent-Class Beta Inflated Models to Assess Students’ Performance: An Italian Case Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 7-18, November.

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