[R-sig-ME] John Nelder and Nelder-Lee HGLMs

[Murray Jorgensen]

The flow seem to have stopped so I will post a few things not already 
noted or expand a little on what has.

Firstly there is the Statistical Science issue with L+N's article and 
discussion:

STATISTICAL SCIENCE
Volume 24, Number 3 August 2009
Likelihood Inference for Models with Unobservables: Another View
Youngjo Lee and John A. Nelder 255

Discussion of Likelihood Inference for Models with Unobservables: 
Another View Thomas A. Louis 270

Discussion of Likelihood Inference for Models with Unobservables: 
Another View Geert Molenberghs, Michael G. Kenward and Geert Verbeke 273

Decoding the H-likelihood Xiao-Li Meng 280

Rejoinder: Likelihood Inference for Models with Unobservables Another 
View  Youngjo Lee and John A. Nelder 294

Secondly John Maindonald mentioned to me the large number of models 
which may be fitted by the MCMCglmm package, using Bayesian methods, of 
course.


Andrew Robinson and Simon Blomberg have already mentioned the interest 
shown by Jim Lindsey in HGLMs.

There are two packages in CRAN: hglm and HGLMM.

I will add that Charles McCulloch has taken some interest in this area. 
There is his JASA article

Maximum Likelihood Algorithms for Generalized Linear Mixed Models
Journal of the American Statistical Association, Vol. 92, No. 437 (Mar., 
1997), pp. 162-170

where he compares Maximum Likelihood methods to "Joint-Maximization" 
methods such as PQL.

and also a recent paper

Prediction of Random Effects in Linear and Generalized Linear
Models under Model Misspecification
Charles E. McCulloch∗ and John M. Neuhaus
(Biometrics Early View, June 2010)

which suggests that GLMMs are fairly robust against the 
mis-specification of the distribution of the random effects.

Murray

-- 
Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
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