Aaron Pribadi
Thesis
Algebraic Methods for Log-Linear Models
- Advisor
- Michael Orrison
- Second Reader(s)
- Weiqing Gu
Abstract
Techniques from representation theory (Diaconis, 1988) and algebraic geometry (Drton et al., 2008) have been applied to the statistical analysis of discrete data with log-linear models. With these ideas in mind, we discuss the selection of sparse log-linear models, especially for binary data and data on other structured sample spaces. When a sample space and its symmetry group satisfy certain conditions, we construct a natural spanning set for the space of functions on the sample space which respects the isotypic decomposition; these vectors may be used in algorithms for model selection. The construction is explicitly carried out for the case of binary data.
Proposal
Markov Bases for Sampling Conditional Distributions
