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Generalized Linear Models pp 160–167Cite as

Implementation of an Algorithm for Fitting a Class of Generalized Logistic Models

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

Summary

A general class of models is proposed for describing the dependence of binary data on explanatory variables, extending the scope of the standard logistic model. A simple and intuitive algorithm for fitting parameters is presented, based on the properties of generalized linear models; implementation is facilitated through the flexibility of GLIM. Asymptotic variance expressions are derived for the parameters and the fitted values. User defined GLIM macros for fitting the model are included and an example is presented.

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References

  • BAKER, R.J. AND NELDER, J.A. (1978). The GLIM Manual-Release 3. Distributed by Numerical Algorithms Group, Oxford.

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  • BLISS, C.I. (1935). The calculation of the dosage-mortality curve. Ann. Appl. Biol. 22, 134–167.

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  • COX, D.R. (1970). The Analysis of Binary Data. Chapman and Hall. London.

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  • NELDER, J.A. and WEDDERBURN, R.W.M. (1972). Generalized Linear Models. JRSS (A) 135, 370–384.

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  • STUKEL, T. (1983). Generalized Logistic Models. Ph.D. thesis. University of Toronto.

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Author information

Authors and Affiliations

  1. Dartmouth Medical School, Hanover, New Hampshire, 03756, USA

    Thérèse Stukel

Authors
  1. Thérèse Stukel
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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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Cite this paper

Stukel, T. (1985). Implementation of an Algorithm for Fitting a Class of Generalized Logistic Models. 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_17

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  • DOI: https://doi.org/10.1007/978-1-4615-7070-7_17

  • 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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