Journal Article

Restoration of Monotonicity Respecting in Dynamic Regression

Huang, Yijian

2017

Overview

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 article, 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.

• Publisher: Taylor & Francis,Taylor & Francis Group,LLC,Taylor & Francis Ltd
• Subject: Adaptive interpolation / Additive complementary log-log survival model / Additive hazards model / Americans / Censored quantile regression / clinical trials / dynamic models / Efficiency / exposure models / Interpolation / Monotone function / Quantile regression / regression analysis / Regression coefficients / Regression models / Statistics / Theory and Methods