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Some Considerations on the Fisher Information in Nonlinear Mixed Effects Models

  • Tobias Mielke
  • Rainer Schwabe
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

The inverse of the Fisher Information Matrix is a lower bound for the covariance matrix of any unbiased estimator of the parameter vector and, given this, it is important for the construction of optimal designs. For normally distributed observation vectors with known variance, the Fisher Information can be easily constructed. For nonlinear mixed effects models, the problem of the missing closed-form solution of the likelihood function carries forward to the calculation of the Fisher Information matrix. The often used approximation of the Fisher Information by linearizing the model-function in the fixed effects case is generally not reliable, as will be shown in this article.

Keywords

Parameter Vector Fisher Information Observation Error Fisher Information Matrix Linear Mixed Effect 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 work was supported by the BMBF grant SKAVOE 03SCPAB3

Refeness

  1. Atkinson, A. C. and R. Cook (1995). D-optimum designs for heteroscedastic linear models. Journal of the American Statistical Association 90, 204–212.MATHCrossRefMathSciNetGoogle Scholar
  2. Davidian, M. and D. Giltinan (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall.Google Scholar
  3. Pinheiro, J. and D. Bates (2000). Mixed-Effects Models in S and S-Plus. New York: Springer-Verlag.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Otto-von-Guericke University MagdeburgMagdeburgGermany

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