Some Considerations on the Fisher Information in N

Authors: Tobias Mielke Rainer Schwabe

Publish Date: 2010

Volume: , Issue: , Pages: 129-136

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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 closedform 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 modelfunction in the fixed effects case is generally not reliable as will be shown in this article

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