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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Vydáno v:Test (Madrid, Spain) Ročník 27; číslo 4; s. 871 - 895
Hlavní autoři: Hendrickx, K., Janssen, P., Verhasselt, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018
Springer Nature B.V
Témata:
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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Popis
Shrnutí: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.
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ISSN:1133-0686
1863-8260
DOI:10.1007/s11749-017-0574-y