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GLIM for Latent Class Analysis

  • Conference paper
Generalized Linear Models

Part of the book series: Lecture Notes in Statistics ((LNS,volume 32))

Summary

We show that latent class models are exponential family nonlinear models. These are extended generalized linear models with the link function substituted by an observationwise defined nonlinear function of the model parameters. Latent class models can be fitted using the OWN-facility in GLIM. We analyse a set of data which Clogg and Goodman (1984) fitted by an EM algorithm. The necessary GLIM macros are discussed.

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References

  • Baker, R. J. and Neider, J. A. (1978) The GLIM System Release 3. Oxford: Numerical Algorithms Group.

    Google Scholar 

  • Burn, R. (1984) Fitting a logit model to data with classification errors. Glim Newsletter, Issue no 8, 44–47.

    Google Scholar 

  • Clogg, C. C. and Goodman, L. A. (1984) Latent structure analysis of a set of multidimensional contingency tables. J. Amer. Statist. Ass. 79, 762–771.

    Article  MathSciNet  MATH  Google Scholar 

  • Cox, C. (1984) Generalized linear models — the missing link. Appl. Statist. 33, 18–24.

    Article  Google Scholar 

  • Green, P. J. (1984) Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. J.R. Statist. Soc. B, 46, 149–192.

    MATH  Google Scholar 

  • Jörgensen, B. (1984a) Maximum likelihood estimation and large-sample inference for generalized linear and nonlinear regression models. Biometrika 70, 19–28.

    Google Scholar 

  • Jörgensen, B. (1984b) The delta algorithm and GLIM. Int. Statist. Rev. 52, 283–300.

    Article  MATH  Google Scholar 

  • McCullagh, P. and Neider, J. A. (1983) Generalized Linear Models. London: Chapman and Hall.

    MATH  Google Scholar 

  • Palmgren, J. and Ekholm, A. (1984). Exponential family nonlinear models for categorical data with errors of observation. Univ. of Helsinki, Dept. of Statist., Research Report n:o 52.

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  • Roger, J. H. (1983) Composite link functions with linear log link and Poisson error. Glim Newsletter, Issue no 7, 15–21.

    Google Scholar 

  • Solomon, H. (1961) Classification procedures based on dichotomous response vectors. In Studies in Item Analysis and Prediction (H. Solomon, ed.). Stanford, Calif.: Stanford University Press.

    Google Scholar 

  • Thompson, R. and Baker, R. J. (1981) Composite link functions in generalized linear models. Appl. Statist. 30, 125–131.

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Author notes

  1. Anders Ekholm

    Present address: Department of Statistics, University of Helsinki, Aleksi 7, SF-00100, Helsinki, Finland

Authors and Affiliations

  1. National Public Health Institute, Finland

    Juni Palmgren

  2. Univ. of Helsinki, Finland

    Anders Ekholm

Authors
  1. Juni Palmgren
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  2. Anders Ekholm
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Statistics and Computing, The Polytechnic of North London, Holloway Road, London, N7 8DB, UK

    Robert Gilchrist , Brian Francis  & Joe Whittaker ,  & 

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© 1985 Springer-Verlag Berlin Heidelberg

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Palmgren, J., Ekholm, A. (1985). GLIM for Latent Class Analysis. In: Gilchrist, R., Francis, B., Whittaker, J. (eds) Generalized Linear Models. Lecture Notes in Statistics, vol 32. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7070-7_14

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  • DOI: https://doi.org/10.1007/978-1-4615-7070-7_14

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-96224-5

  • Online ISBN: 978-1-4615-7070-7

  • eBook Packages: Springer Book Archive

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