SMS scnews item created by John Ormerod at Thu 22 May 2014 1027
Type: Seminar
Distribution: World
Expiry: 31 May 2014
Calendar1: 30 May 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod4.pc (assumed)

# Statistics Seminar: Ian Marschner - Macquarie University -- Constrained GLMs using combinatorial EM algorithms with biostatistical applications


Motivated by applications in biostatistics, I will discuss computational methods
for various constrained generalized linear models (GLMs) in which the linear
predictor cannot range over the entire real line. Common examples include the
binomial model with log or identity link, and the Poisson model with identity
relative risks, risk differences and rate differences. I will begin by
illustrating the surprisingly unstable iterative behavior exhibited by
conventional GLM software (primarily using R). This instability stems from the
fact that Fisher scoring may have a repelling fixed point for such
non-canonical models, which can induce periodicity and chaos in the iterative
sequence. I will then discuss a class of algorithms called combinatorial EM
(CEM) algorithms, which are an extension of the standard EM algorithm. CEM
algorithms provide a stable alternative to standard GLM algorithms and are
particularly suited to semi-parametric extensions through generalized additive
models. I will primarily use the log link binomial model as a case study,
including some practical data analysis examples, but I will also mention how CEM
algorithms apply to other models.


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