Sieve maximum likelihood regression analysis of dependent current status data
From MaRDI portal
Publication:3455821
DOI10.1093/biomet/asv020zbMath1452.62832OpenAlexW2275112306MaRDI QIDQ3455821
Publication date: 11 December 2015
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/asv020
Asymptotic properties of parametric estimators (62F12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01)
Related Items (36)
Regression analysis of informative current status data with the semiparametric linear transformation model ⋮ An additive hazards cure model with informative interval censoring ⋮ Regression analysis of current status data with latent variables ⋮ Regression analysis of dependent current status data with the accelerated failure time model ⋮ A new method for regression analysis of interval-censored data with the additive hazards model ⋮ Variable Selection of Interval-Censored Failure Time Data ⋮ A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments Under Weibull Lifetimes ⋮ Sieve maximum likelihood estimation for the proportional hazards model under informative censoring ⋮ Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments ⋮ A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates ⋮ Regression analysis of case II interval-censored data with auxiliary covariates ⋮ Estimation of the additive hazards model with linear inequality restrictions based on current status data ⋮ Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data ⋮ Sharp minimax distribution estimation for current status censoring with or without missing ⋮ Regression analysis of interval-censored failure time data with cured subgroup and mismeasured covariates ⋮ Estimation of complier causal treatment effects with informatively interval-censored failure time data ⋮ Regression analysis of multivariate current status data under a varying coefficients additive hazards frailty model ⋮ New methods for the additive hazards model with the informatively interval‐censored failure time data ⋮ Variable Selection for Interval‐censored Failure Time Data ⋮ Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring ⋮ Cluster non‐Gaussian functional data ⋮ A new approach for regression analysis of multivariate current status data with informative censoring ⋮ Regression analysis of misclassified current status data with informative observation times ⋮ Generalized Odds Rate Frailty Models for Current Status Data with Informative Censoring ⋮ Estimation of linear transformation cure models with informatively interval-censored failure time data ⋮ Combined estimating equation approaches for the additive hazards model with left-truncated and interval-censored data ⋮ Bayesian empirical analysis of the proportional hazards model for right-censored failure time data ⋮ Joint analysis of interval-censored failure time data and panel count data ⋮ Inference on semiparametric transformation model with general interval-censored failure time data ⋮ Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression ⋮ Generalized accelerated hazards mixture cure models with interval-censored data ⋮ Analysis of Gap Times Based on Panel Count Data With Informative Observation Times and Unknown Start Time ⋮ Survival function estimation of current status data with dependent censoring ⋮ Estimation of the additive hazards model with case \(K\) interval-censored failure time data in the presence of informative censoring ⋮ Semiparametric sieve maximum likelihood estimation for accelerated hazards model with interval-censored data ⋮ Regression analysis of case-cohort studies in the presence of dependent interval censoring
This page was built for publication: Sieve maximum likelihood regression analysis of dependent current status data