Statistics and Data Science Seminar
Prof. Mathias Drton
University of Chicago
Hypothesis tests in algebraic statistical models
Abstract: Many statistical models are defined in terms of polynomial constraints,
or in terms of polynomial or rational parametrizations. Such algebraic
models include, for instance, factor analysis and instrumental variable
models, latent class models, and more generally, discrete and Gaussian
graphical models with hidden variables. Statistical inference in hidden
variable models is complicated by the fact that the models' parameter
spaces are typically not smooth. This is the motivation for this talk
that considers testing a null hypothesis with singularities in algebraic
models. The focus will be on the large-sample asymptotic behavior of
likelihood ratio and Wald tests.
Wednesday October 1, 2008 at 4:15 PM in SEO 612