- Linear and generalized linear models, exponential families.
- Asymptotic approximation to the distribution of estimators.
- Variance components and structured covariance models.
- Spatial models in agricultural applications, particularly models that are closed under conformal transformation.
- Category theory and projective systems: stochastic processes, regression processes, causality.
- Representation-theory for normal categories: Linear models, factorial models, homologous factors.
- Random objects: sequences, subsets, partitions, trees, arrays, matrices,...
- Notions of exchangeability and partial exchangeability; relation to categories.
- Foundations of statistical models: Functorial definition.
- Monte-Carlo integration as an application of a statistical model.
Last update: 3/22/16