Penalized spline estimation in varying coefficient models with censored data

K. Hendrickx (), P. Janssen and A. Verhasselt
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K. Hendrickx: Hasselt University, I-BioStat
P. Janssen: Hasselt University, I-BioStat
A. Verhasselt: Hasselt University, I-BioStat

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 4, No 10, 895 pages

Abstract: 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.

Keywords: Censoring; Nonparametric statistics; P-splines; Regularization; Varying coefficient model; 62G08; 62G20; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11749-017-0574-y

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