报告人: Liqun Wang(加拿大马尼托巴大学）
In this talk, I will first show some examples of measurement error and demonstrate its impact in linear and logistic regression models. Then I'll introduce the instrumental variable method for bias correction and apply it to various models with complex data structures that are widely used in practical data analysis. Finally, this method is extended to the high-dimensional variable selection problem.
Liqun Wang is Professor and Head of the Department of Statistics at the University of Manitoba, Canada.He has been an Editor-in-Chief of Springer Journal Statistical Papers, an Associate Editor of Canadian Journal of Statistics, and a Guest Editor of the International Journal of Quality Technology and Quantitative Management.His current research areas include boundary crossing probability (first passage time) for diffusion processes, identification and estimation in nonlinear measurement error models and in longitudinal data models with missing data, high-dimensional variable selection and data assimilation, and Monte Carlo simulation methods in statistical computation and optimization.