Simple linear models are important and relatively easy to
deal with, but there are many situations where they are
not appropriate. Generalized linear models make up a
considerably larger class of models, which are amendable to analysis, and
which cover a wide variety of important applications,
such as modeling binary or polytomous data or investigating
the detailed structure of higher way contingency tables.
Different methods of Bootstrap estimation are considered
for this class of models.
Their properties are studied by comparing
asymptotical results or by simulation techniques.
We are now working on modifications of the nonparametric
maximum-likelihood estimate in Generalized Linear Models
including Models for overdispersed data, Random Effect
Models and Hierarchical Models. A Bootstrap based on the
respective estimating equations will be developed and will
provide small sample support for the new estimating method.