10AM - Halsey Hall, Room 120
On Semiparametric Transformation Models and their Applications
Abstract: Non-normally distributed data frequently occur in many applications. A useful class of models for handling non-normal data is the semiparametric transformation models. Under these models, after an unknown strictly increasing transformation, the outcome variable is assumed to be linearly related to the covariates. This class of models includes commonly-used survival models such as the Cox proportional hazards model and the proportional odds model as special cases. In this talk, we provide an overview of the semiparametric transformation models and discuss the likelihood-based estimation and inference procedures. Applications to diagnostic medicine and neuroimaging are provided to demonstrate the flexibility and usefulness of these models.