Selecting the smoothing parameter and knots for an extension of penalized splines to censored data

DOI10.1080/00949655.2021.1913737OpenAlexW3156778712MaRDI QIDQ3389650

Jesus Orbe, Jorge Virto

Publication date: 23 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2021.1913737

zbMATH Keywords

survival analysiscensored datanonparametric estimationpenalized splinesKaplan-Meier weights

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Unnamed Item ⋮ Automatic search intervals for the smoothing parameter in penalized splines

Uses Software

• R
• survival
• SemiPar
• FITPACK
• gamair

Cites Work

• A statistical perspective on ill-posed inverse problems (with discussion)
• Regression analysis with randomly right-censored data
• Asymptotic normality of the `synthetic data' regression estimator for censored survival data
• Modeling survival data: extending the Cox model
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Consistent estimation under random censorship when covariables are present
• Nonparametric Estimation from Incomplete Observations
• Flexible smoothing with P-splines: a unified approach
• Flexible Parsimonious Smoothing and Additive Modeling
• Fast Computation of Fully Automated Log-Density and Log-Hazard Estimators
• Semiparametric Regression
• Smoothing Spline Gaussian Regression: More Scalable Computation via Efficient Approximation
• Penalized spline smoothing using Kaplan–Meier weights with censored data
• Modified spline regression based on randomly right-censored data: A comparative study
• A practical guide to splines.
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