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SUMMARY:Approximation of the Fisher information and design in nonlinear mi
 xed effects models - Mielke\, T (Otto-von-Guericke)
DTSTART:20110812T100000Z
DTEND:20110812T104500Z
UID:TALK32331@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The missing closed form representation of the probability dens
 ity of the observations is one main problem in the analysis of Nonlinear M
 ixed Effects Models. Often local approximations based on linearizations of
  the model are used to approximately describe the properties of estimators
 . The Fisher Information is of special interest for designing experiments\
 , as its inverse yields a lower bound of the variance of any unbiased esti
 mator. Different linearization approaches for the model yield different ap
 proximations of the true underlying stochastical model and the Fisher Info
 rmation (Mielke and Schwabe (2010)). \nTarget of the presentation are alte
 rnative motivations of Fisher-Information approximations\, based on condit
 ional moments. For an individual design\, known inter-individual variance 
 and intra-individual variance\, the Fisher Information for estimating the 
 population location parameter vector results in an expression depending on
  conditional moments\, such that approximations of the expectation of the 
 conditional variance and the variance of the conditional expectation yield
  approximations of the Fisher Information\, which are less based on distri
 bution assumptions. \nTierney et. al. (1986) described fully exponential L
 aplace approximations as an accurate method for approximating posterior mo
 ments and densities in Bayesian models. We present approximations of the F
 isher Information\, obtained by approximations of conditional moments with
  a similar heuristic and compare the impact of different Fisher Informatio
 n approximations on the optimal design for estimating the population locat
 ion parameters in pharmacokinetic studies.\n\n
LOCATION:Seminar Room 1\, Newton Institute
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