Nonparametric Estimation of the Multivariate Distribution Function in a Censored Regression Model with Applications
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Publication:3017872
DOI10.1080/03610926.2010.489175zbMath1318.62297OpenAlexW2012917400MaRDI QIDQ3017872
Publication date: 20 July 2011
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2010.489175
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Estimation in survival analysis and censored data (62N02)
Related Items (11)
A dimension reduction approach for conditional Kaplan-Meier estimators ⋮ Tree-based censored regression with applications in insurance ⋮ Single index regression models in the presence of censoring depending on the covariates ⋮ Partial identification and inference in censored quantile regression ⋮ A likelihood-based approach for cure regression models ⋮ Double-slicing assisted sufficient dimension reduction for high-dimensional censored data ⋮ A general approach for cure models in survival analysis ⋮ A generalization of the Kaplan-Meier estimator for analyzing bivariate mortality under right-censoring and left-truncation with applications in model-checking for survival copula models ⋮ Unnamed Item ⋮ Generalized M-estimation for the accelerated failure time model ⋮ An Adapted Loss Function for Censored Quantile Regression
Cites Work
- An estimate on the supremum of a nice class of stochastic integrals and U-statistics
- Nonparametric lack-of-fit tests for parametric mean-regression models with censored data
- Polynomial regression with censored data based on preliminary nonparametric estimation
- Large sample behaviour of the product-limit estimator on the whole line
- On almost sure convergence of conditional empirical distribution functions
- Some asymptotic properties of kernel estimators of a density function in case of censored data
- Asymptotics of conditional empirical processes
- A remark concerning strong uniform consistency of the conditional Kaplan- Meier estimator
- Strong embedding of the estimator of the distribution function under random censorship
- Regression analysis with randomly right-censored data
- Uniform consistency of the kernel conditional Kaplan-Meier estimate
- Estimation and bootstrap with censored data in fixed design nonparametric regression
- The strong law under random censorship
- Maximal inequalities for degenerate \(U\)-processes with applications to optimization estimators
- Transfer of tail information in censored regression models
- Nearest neighbor estimation of a bivariate distribution under random censoring
- Unified methods for censored longitudinal data and causality
- The central limit theorem under random censorship
- An approach to nonparametric regression for life history data using local linear fitting
- Kernel estimation in a nonparametric marker dependent hazard model
- Weak convergence and empirical processes. With applications to statistics
- The product-limit estimator and the bootstrap: Some asymptotic representations
- Consistent estimation under random censorship when covariables are present
- Uniform in bandwidth consistency of kernel-type function estimators
- Inference for a nonlinear counting process regression model
- Asymptotic properties of a generalized kaplan-meier estimator with some applications
- Simulation and the Asymptotics of Optimization Estimators
- Nonlinear Censored Regression Using Synthetic Data
- Some asymptotic properties of nonparametric regression estimators in case of randomly censored data
- The Kaplan–Meier Estimator as an Inverse-Probability-of-Censoring Weighted Average
- Uniform Representation of Product‐Limit Integrals with Applications
- An empirical process approach to the uniform consistency of kernel-type function estimators
- The central limit theorem under censoring
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