|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.
Statistics seminar: Miroslaw Pawlak: “Nearest Neighbor Estimates for Nonlinear Time Series Systems” — Thursday, November 23 at 2:45 p.m., 301 Biological Sciences.
PIMS lecture: Anthony Bonato — Thursday, November 30 at 4 p.m., Robert Schultz Theatre.
Interdisciplinary seminar: Lena Kourkoutis — Friday, December 1 at 4 p.m., Robert Schultz Theatre.