Penalized spline estimation in varying coefficient models with censored data

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 consi...

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Veröffentlicht in:Test (Madrid, Spain) Jg. 27; H. 4; S. 871 - 895
Hauptverfasser: Hendrickx, K., Janssen, P., Verhasselt, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018
Springer Nature B.V
Schlagworte:
Censored data (mathematics)
Coefficients
Computer simulation
Data smoothing
Economic models
Economics
Estimators
Finance
Insurance
Knots
Management
Mathematics and Statistics
Nonparametric statistics
Normality
Original Paper
Regression analysis
Regression models
Statistical Theory and Methods
Statistics
Statistics for Business
Transformations
P-splines
62G08
62N01
Censoring
62N02
62G20
Regularization
Nonparametric statistics
Varying coefficient model
ISSN:1133-0686, 1863-8260
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1133-0686
1863-8260
DOI:10.1007/s11749-017-0574-y