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SUMMARY:Lecture on estimation and inference: Non-maximum likelihood estima
 tion and statistical inference for linear and nonlinear mixed models - Dem
 idenko\, E (Dartmouth College)
DTSTART:20110810T083000Z
DTEND:20110810T093000Z
UID:TALK32302@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Traditionally linear and nonlinear mixed effects models are es
 timated by maximum likelihood assuming normal distribution. The goal of th
 is lecture is to discuss non-iterative methods for estimation of linear mi
 xed models and simplified methods for estimation of generalized linear and
  nonlinear mixed models. In particular\, we will talk about testing the pr
 esence of random effects\, often overlooked fundamental test in the framew
 ork of mixed effects model. Simplified methods for generalized linear mixe
 d models (GLMM)\, such as conditional logistic regression models with rand
 om intercepts and Poisson model for count data will be discussed. Limitati
 ons of popular generalized estimating equation (GEE) approach are uncovere
 d. On the other hand\, it is shown that this approach is valid for Poisson
  mixed model. Fixed sample maximum likelihood approach is introduced and i
 ts statistical properties are investigated via statistical simulations. Op
 en problems and future work on statistical inference for mixed models are 
 outlined.\n
LOCATION:Seminar Room 1\, Newton Institute
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