Nonparametric density estimation for censored survival data: Regression‐spline approach

DOI10.2307/3315466zbMath0754.62017OpenAlexW2089220569WikidataQ58161680 ScholiaQ58161680MaRDI QIDQ4021169

Antonio Ciampi, Michał Abrahamowicz, James O. Ramsay

Publication date: 17 January 1993

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315466

zbMATH Keywords

density estimationcensored survival dataregression splinesspline smoothingminimum AIC\(I\)-splinesrandomly censored samplelinear combination of cubic \(M\)-splinespseudo-maximum- likelihood estimationsmall number of knots

Mathematics Subject Classification ID

Density estimation (62G07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Information theoretic criteria in non-parametric density estimation. Bias and variance in the infinite dimensional case ⋮ Comparison of imputation methods for interval censored time-to-event data in joint modelling of tree growth and mortality ⋮ A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware ⋮ Bayesian nonparametric quantile process regression and estimation of marginal quantile effects ⋮ A Censored Data Histogram

Cites Work

• Density estimation in the simple proportional hazards model
• On Polya frequency functions. IV: The fundamental spline functions and their limits
• Survival Estimation Using Splines
• Splines in Statistics
• Nonparametric density estimation from censored data
• Smoothing hazard rates with cubic splines
• Small sample relative performance of the spline smooth survival estimator
• Restricted Mean Life With Adjustment for Covariates
• Extended Hazard Regression for Censored Survival Data with Covariates: A Spline Approximation for the Baseline Hazard Function
• Density estimation under the koziol‐green model of censoring by penalized likelihood methods
• A new look at the statistical model identification
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