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Some Considerations on the Fisher Information in Nonlinear Mixed Effects Models
BibTeX
@MISC{Mielke_someconsiderations,
author = {T. Mielke and R. Schwabe},
title = {Some Considerations on the Fisher Information in Nonlinear Mixed Effects Models},
year = {}
}
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Abstract
Abstract The inverse of the Fisher Information Matrix is a lower bound for the co-variance matrix of any unbiased estimator of the parameter vector and, given this, it is important for the construction of optimal designs. For normally distributed ob-servation vectors with known variance, the Fisher Information can be easily con-structed. 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 is generally not reliable, as will be shown in this article. 1
