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Tests on Discrete Data

  • Thomas Haslwanter
Chapter
  • 16k Downloads
Part of the Statistics and Computing book series (SCO)

Abstract

Generalized Linear Models (GLMs) substantially extend the power of statistical modeling. This chapter introduces GLMs and shows how to implement logistic regression, a frequently used application of GLMs, with the tools provided by Python. A worked example of Ordinal Logistic Regression demonstrates how the package “scikit-learn” and the tools of machine learning can be used for statistical modeling.

Keywords

Ordinal Logistic Regression Model Generalized Linear Models (GLMs) Exponential Family Proportional Odds Model Linear Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Dobson, A. J., & Barnett, A. (2008). An introduction to generalized linear models (3rd ed.). Boca Raton: CRC Press.zbMATHGoogle Scholar
  2. McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society. Series B (Methodological), 42(2):109–142.MathSciNetzbMATHGoogle Scholar
  3. McCullagh, P. & Nelder, J. A. (1989). Generalized linear models (2nd ed.). New York: Springer.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas Haslwanter
    • 1
  1. 1.School of Applied Health and Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

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