Estimation and inference on semiparametric regression models
Department of Biostatistics
Johnson Center 326-Meeting Room B
4400 University Drive, Fairfax, VA 22030
Time: 11:00 A.M. - 12:00 P.M.
Date: Friday, Mar 7, 2014
For regression models, it is challenging to obtain point and variance estimates of regression parameters if the corresponding estimating functions are nonregular, such as non-smooth and non-monotone. We discuss the issues and present recently developed new approaches based on a natural self-induced smoothing method. We show general theory, implementation, simulation studies and demonstrate the methods with censored linear regression models and general semiparametric transformation models.