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Penalized spline estimation in varying coefficient models with censored data

Research paper by K. Hendrickx, P. Janssen, A. Verhasselt

Indexed on: 22 Dec '17Published on: 20 Dec '17Published in: TEST



Abstract

We consider P-spline smoothing in a varying coefficient regression model when the response is subject to random right censoring. We introduce two data transformation approaches to construct a synthetic response vector that is used in a penalized least squares optimization problem. We prove the consistency and asymptotic normality of the P-spline estimators for a diverging number of knots and show by simulation studies and real data examples that the combination of a data transformation for censored observations with P-spline smoothing leads to good estimators of the varying coefficient functions.