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Discrimination Between Random and Fixed Effect Logistic Regression Models

  • Chiara Tommasi
  • Maria Teresa Santos-Martín
  • Juan Manuel Rodríguez-Díaz
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

D s- and KL-optimum designs are computed for discriminating between univariate logistic regression models with or without random effects. Both these competing optimum designs are constructed numerically. The main problem in finding them is the computation of some integrals at each step of the numerical procedure. In order to improve the convergence speed of this numerical procedure some integral approximations are suggested.

Keywords

Logistic Regression Model Directional Derivative Fisher Information Matrix Rival Model Univariate Logistic Regression Model 
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.

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Notes

Acknowledgements

This research was supported by the Spanish Junta de Castilla y León (Project ‘SA071A09’) and the Spanish Ministry of Education and Science (Projects ‘MTM 2007-672111- C03-01’ and ‘Ingenio Mathematica (i-MATH)’ No. CSD2006-00032, Consolider - Ingenio 2010).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chiara Tommasi
    • 1
  • Maria Teresa Santos-Martín
    • 2
  • Juan Manuel Rodríguez-Díaz
    • 2
  1. 1.Department of Economics, Business and StatisticsUniversity of MilanMilanItaly
  2. 2.Department of StatisticsUniversity of SalamancaSalamancaSpain

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