Asymptotic representation of presmoothed Kaplan-Meier integrals with covariates in a semiparametric censorship model
From MaRDI portal
Publication:254198
DOI10.1016/J.JSPI.2015.12.001zbMath1334.62159OpenAlexW2203916206MaRDI QIDQ254198
Gerhard Dikta, Jacobo de Uña-Álvarez, Jorge Mendonça, René Külheim
Publication date: 8 March 2016
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2015.12.001
Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Asymptotically efficient estimation under semi-parametric random censorship models
- Presmoothing the transition probabilities in the illness-death model
- Nonparametric estimation of the conditional distribution function in a semiparametric censorship model
- On semiparametric random censorship models
- The central limit theorem under semiparametric random censorship models
- The central limit theorem under random censorship
- Presmoothing the Aalen-Johansen estimator in the illness-death model
- Consistent estimation under random censorship when covariables are present
- A semiparametric estimator of the bivariate distribution function for censored gap times
- Weak representation of the cumulative hazard function under semiparametric random censorship models
- Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present
- Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log‐Rank Tests
- Bootstrap Approximations in Model Checks for Binary Data
This page was built for publication: Asymptotic representation of presmoothed Kaplan-Meier integrals with covariates in a semiparametric censorship model
