Semiparametric regression analysis for clustered failure time data
From MaRDI portal
Publication:2739336
DOI10.1093/biomet/87.4.867zbMath1028.62025OpenAlexW2066480653MaRDI QIDQ2739336
No author found.
Publication date: 9 September 2001
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/87.4.867
predictionGaussian processcensoringproportional hazards modelproportional odds modelKaplan-Meier estimatelinear transformation model
Nonparametric regression and quantile regression (62G08) Estimation in survival analysis and censored data (62N02)
Related Items (43)
The semiparametric accelerated trend-renewal process for recurrent event data ⋮ Partial rank estimation of duration models with general forms of censoring ⋮ Sequential Analysis of Censored Data with Linear Transformation Models ⋮ Empirical likelihood of conditional quantile difference with left-truncated and dependent data ⋮ Jackknife empirical likelihood for linear transformation models with right censoring ⋮ On Fitting Transformation Model to Survey Data ⋮ An additive marginal regression model for clustered recurrent event in the presence of a terminal event ⋮ Nonparametric tests for multistate processes with clustered data ⋮ Asymptotic normality of conditional density estimation under truncated, censored and dependent data ⋮ Weighted Estimator for the Linear Transformation Models with Multivariate Failure Time Data ⋮ Confidence interval for the difference between two median survival times with semiparametric transformation models ⋮ A flexible semiparametric transformation model for survival data ⋮ Empirical likelihood for conditional quantile with left-truncated and dependent data ⋮ Linear transformation models for censored data under truncation ⋮ Kernel estimation of conditional density with truncated, censored and dependent data ⋮ Marginal Proportional Hazards Models for Clustered Interval-Censored Data with Time-Dependent Covariates ⋮ Non‐parametric regression in clustered multistate current status data with informative cluster size ⋮ New empirical likelihood inference for linear transformation models ⋮ Conditional quantile estimation with truncated, censored and dependent data ⋮ Asymptotic properties of conditional distribution estimator with truncated, censored and dependent data ⋮ Semiparametric methods for clustered recurrent event data ⋮ Quantile Regression Models with Multivariate Failure Time Data ⋮ Asymptotic Properties of Conditional Quantile Estimator Under Left-Truncated and α-Mixing Conditions ⋮ Nonlinear wavelet estimator of the regression function under left-truncated dependent data ⋮ A semiparametric linear transformation model to estimate causal effects for survival data ⋮ An additive hazards model for clustered recurrent gap times ⋮ Asymptotic normality of wavelet density estimator under censored dependent observations ⋮ Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is Large ⋮ Semiparametric regression analysis for clustered doubly-censored data ⋮ Semiparametric inference for transformation models via empirical likelihood ⋮ Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes ⋮ Conditional quantile estimation with auxiliary information for left-truncated and dependent data ⋮ Checking the linear transformation model for clustered failure time observations ⋮ Modeling Multivariate Survival Data by a Semiparametric Random Effects Proportional Odds Model ⋮ Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction ⋮ Nonlinear wavelet density estimation with censored dependent data ⋮ Local polynomial quasi-likelihood regression with truncated and dependent data ⋮ Additive Transformation Models for Multivariate Interval-Censored Data ⋮ Additive Transformation Models for Clustered Doubly Censored Data ⋮ Semiparametric analysis of transformation models with dependently left-truncated and right-censored data ⋮ Regression analysis of clustered interval-censored failure time data with the additive hazards model ⋮ Global L2 error of wavelet density estimator with truncated and strong mixing observations ⋮ Asymptotic normality of conditional density estimation with left-truncated and dependent data
This page was built for publication: Semiparametric regression analysis for clustered failure time data