STATISTICS COLLOQUIUM
Liqun Wang
Department of Statistics
University of Manitoba
Variable selection and estimation in generalized linear
models with measurement error
Abstract
We study the variable selection and estimation problems in linear and generalized linear models when some of the predictors are measured with error. We demonstrate how measurement error impacts the selection results and propose regularized instrumental variable methods to correct for the measurement error effects. The proposed methods are consistent in selection and estimation and we derive their asymptotic distributions under general conditions. We also investigate the performances of the methods through Monte Carlo simulations and compare them with the naive method that ignores measurement error. Finally, the proposed method is applied to a real dataset. This is a joint work with Lin Xue.
DATE: Friday, November 1, 2019
TIME: 11:00 am
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 10:30 am in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu