|Date:||Thursday, April 6, 2017|
Dynamic regression models, including the quantile regression model and Aalen's additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this talk, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided.
October 5 – October 6: Fall Term break (No classes)
PIMS lecture: Robert Serfling: “Depth Functions in Multivariate & Other Data Settings: Concepts, Perspectives, Challenges” — Thursday, September 28 at 4 p.m., Robert Schultz Theatre.
Statistics seminar: Zeinab Mashreghi: “Resampling procedures in the presence of missing data applied to high entropy sampling designs” — Thursday, October 12 at 2:45 p.m., 301 Biological Sciences.
Statistics seminar: Francis Zwiers — Thursday, October 19 at 2:45 p.m., 301 Biological Sciences.
PIMS lecture: Melania Alvarez — Thursday, October 26 at 4 p.m., Robert Schultz Theatre.