Nonlinear Censored Regression Using Synthetic Data

DOI10.1111/j.1467-9469.2007.00591.xzbMath1164.62024OpenAlexW2063345462MaRDI QIDQ3608265

Unnamed Author, Valentin Patilea, Michel Delecroix

Publication date: 28 February 2009

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

Full work available at URL: http://crest.science/RePEc/wpstorage/2006-10.pdf

zbMATH Keywords

asymptotic normalityKaplan-Meyer estimateleast squares estimation with synthetic data

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Censored data models (62N01) General nonlinear regression (62J02) Estimation in survival analysis and censored data (62N02)

Related Items (16)

Nonparametric robust regression estimation for censored data ⋮ The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables ⋮ A weighted quantile regression for nonlinear models with randomly censored data ⋮ A likelihood-based approach for cure regression models ⋮ Adaptive estimation of the conditional intensity of marker-dependent counting processes ⋮ Bivariate censored regression relying on a new estimator of the joint distribution function ⋮ Conditional density estimation in a censored single-index regression model ⋮ Sufficient dimension reduction on marginal regression for gaps of recurrent events ⋮ New estimators of the extreme value index under random right censoring, for heavy-tailed distributions ⋮ Nonparametric Estimation of the Multivariate Distribution Function in a Censored Regression Model with Applications ⋮ Nonparametric lack-of-fit tests for parametric mean-regression models with censored data ⋮ Guided Censored Regression ⋮ Modified estimators in semiparametric regression models with right-censored data ⋮ Single-index regression models with right-censored responses ⋮ Nonparametric relative regression under random censorship model ⋮ Weighted Least‐Squares Method for Right‐Censored Data in Accelerated Failure Time Model

Cites Work

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• Universal Gaussian approximations under random censorship
• A Dvoretzky-Kiefer-Wolfowitz type inequality for the Kaplan-Meier estimator.
• The central limit theorem under random censorship
• Weak convergence and empirical processes. With applications to statistics
• Consistent estimation under random censorship when covariables are present
• Linear Models, Random Censoring and Synthetic Data
• M-estimation in censored linear models
• Censored Regression: Local Linear Approximations and Their Applications
• The Kaplan–Meier Estimator as an Inverse-Probability-of-Censoring Weighted Average
• Asymptotic Properties of Non-Linear Least Squares Estimators

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