|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.
March 31: Last date for Voluntary Withdrawal (VW) from Winter Term and Fall/Winter Term spanned courses.
April 3: Summer Term registration begins
Statistics seminar: Lei Sun: “Testing a vector of parameters with applications to genetic association studies” — Thursday, March 30 at 2:45 p.m., 527 Buller.
Statistics seminar: Eugene Huang: “Restoration of Monotonicity Respecting in Dynamic Regression” — Thursday, April 6 at 2:45 p.m., 527 Buller.
Data talk: Ehsan Khafipour — Thursday, April 13 at 2:45 p.m., 527 Buller.
Statistics seminar: Saumen Mandal — Thursday, April 20 at 2:45 p.m., 527 Buller.