Generalized linear mixed models, fall 2014
Juha Alho, professor
The course provides an introduction to methods used in causal research, when the outcome of interest might be a binomial “yes/no” or a Poisson distributed count. The dependencies arise when experimental units form groups (such as members of a family, repeated measures from the same individual, or measures from close locations). The dependencies are modeled via random latent factors that are shared by the experimental units. The term “mixed” refers to the presence of both fixed and random effects in the generalized linear model. The application of likelihood methods into generalized linear models can be tedious as the required marginal models must be evaluated numerically. A large number of approximations are in use, but the results rely on asymptotics that may not always be tenable in practical applications. An effective alternative analysis is sometimes available via Bayesian formulations. R package is used for computing.
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