Parameter estimation of partial linear model under monotonicity constraints with censored data

Abstract

We propose a weighted least square method for estimation in the partial linear model with monotonicity constraints and right-censored data. This method uses the Kaplan–Meier weights to account for censoring and monotone B-splines to approximate the unknown monotone function. We show that the proposed estimator of regression coefficients is root-n consistent and asymptotically normal under appropriate assumptions. One advantage is that our method can be easily computed using existing software. A simulation study is conducted to evaluate the finite sample performance of the proposed method.