Improved conditional approximations of the population Fisher information matrix

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• Nyberg, J (Uppsala)
• Friday 12 August 2011, 11:45-12:30
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

We present an extended approximation of the Fisher Information Matrix (FIM) for nonlinear mixed effects models based on a first order conditional (FOCE) approximation of the population likelihood. Unlike previous FOCE based FIM , we use the empirical Bayes estimates to derive the FIM . In several examples, compared to the old FOCE based FIM , the improved FIM predicts parameter uncertainty much closer to simulation based empirical parameter uncertainty. Furthermore, this approach seems more robust against other approximations of the FIM , i.e. (Full/Reduced FIM ). Finally, the new FOCE derived FIM is slightly closer to the simulated empirical precision than the FO based FIM .

This talk is part of the Isaac Newton Institute Seminar Series series.

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