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Some Issues in Generalized Linear Modeling

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Matrices, Statistics and Big Data (IWMS 2016)

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

This chapter discusses cautions, questions, challenges, and proposals regarding five issues that arise in generalized linear modeling. With primary emphasis on categorical data, we summarize (1) bias that can occur in using ordinary linear models with ordinal response variables, (2) a new proposal about simple ways to interpret effects in generalized linear models that use nonlinear link functions, (3) problems with using Wald significance tests and confidence intervals, (4) a question about the behavior of residuals for generalized linear models, and (5) a new approach in using generalized estimating equations (GEE) methods for marginal multinomial models.

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Author information

Authors and Affiliations

  1. University of Florida, Gainesville, FL, USA

    Alan Agresti

Authors
  1. Alan Agresti
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Correspondence to Alan Agresti .

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Editors and Affiliations

  1. Department of Mathematics and Statistics, Brock University, St. Catharines, ON, Canada

    S. Ejaz Ahmed

  2. Departmental Unit of Mathematics and Physics, Polytechnic Institute of Tomar, Tomar, Portugal

    Francisco Carvalho

  3. Faculty of Natural Sciences, University of Tampere, Tampere, Finland

    Simo Puntanen

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Agresti, A. (2019). Some Issues in Generalized Linear Modeling. In: Ahmed, S., Carvalho, F., Puntanen, S. (eds) Matrices, Statistics and Big Data. IWMS 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-17519-1_6

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