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Simultaneous inference in general parametric models.

Torsten Hothorn1, Frank Bretz, Peter Westfall

  • 1Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, D-80539 München, Germany. Torsten.Hothorn@stat.uni-muenchen.de

Biometrical Journal. Biometrische Zeitschrift
|May 16, 2008
PubMed
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Simultaneous inference procedures are essential for controlling errors when testing multiple hypotheses. This study presents a general framework for these procedures across various statistical models, enhancing reliable data analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Simultaneous inference is crucial in applications with multiple hypothesis tests.
  • Testing multiple hypotheses increases the risk of erroneous rejections (Type I errors).
  • Controlling the overall Type I error rate requires specialized adjustment procedures.

Purpose of the Study:

  • To describe simultaneous inference procedures within general parametric models.
  • To extend existing multiple comparison procedures to a broader range of statistical models.
  • To provide a unified framework for simultaneous inference.

Main Methods:

  • Development of a general framework for simultaneous inference in parametric models.
  • Specification of experimental questions via linear combinations of model parameters.

Related Experiment Videos

  • Utilizing the R add-on package 'multcomp' for practical implementation.

    • Main Results:

    • The proposed framework generalizes simultaneous inference beyond ANOVA models.
    • It encompasses linear regression, generalized linear models, mixed-effects models, Cox models, and robust linear models.
    • Demonstrated applicability through diverse examples across various statistical models.

    Conclusions:

    • The presented general approach offers a unified and flexible method for simultaneous inference.
    • The 'multcomp' package provides a convenient interface for applying these procedures.
    • This framework enhances the reliability of statistical conclusions in complex data analyses.