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Improved conditional approximations of the population Fisher information matrix

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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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Martin Ryle Seminar Room, Kavli Institute Institute of Metabolic Science (IMS), Level 4 ATC, Seminar rooms 1,2 & 3 MR4, Centre for Mathematical Sciences, Wilberforce Road, Cambridge

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