A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data

DOI10.1214/11-AOS934zbMath1246.62103arXiv1203.2470WikidataQ61034669 ScholiaQ61034669MaRDI QIDQ449981

Ying Ding, Bin Nan

Publication date: 3 September 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1203.2470

zbMATH Keywords

B-splinesemiparametric efficiencyaccelerated failure time modelefficient score functionsieve maximum likelihood estimation

Mathematics Subject Classification ID

Related Items (18)

A lack-of-fit test for generalized linear models via single-index techniques ⋮ Current status linear regression ⋮ Estimating the error distribution in semiparametric transformation models ⋮ Laplace approximated quasi-likelihood method for heteroscedastic survival data ⋮ Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration ⋮ Nonparametric analysis of a duration model with stochastic unobserved heterogeneity ⋮ Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension ⋮ Survival Analysis via Ordinary Differential Equations ⋮ Sieve estimation of semiparametric accelerated mean models with panel count data ⋮ Joint analysis of interval-censored failure time data and panel count data ⋮ A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data ⋮ A SIMPLE ITERATIVE Z-ESTIMATOR FOR SEMIPARAMETRIC MODELS ⋮ Generalized accelerated hazards mixture cure models with interval-censored data ⋮ Guided Censored Regression ⋮ Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression ⋮ Semiparametric \(M\)-estimation with non-smooth criterion functions ⋮ Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors ⋮ Estimating Mean Survival Time: When is it Possible?

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Two likelihood-based semiparametric estimation methods for panel count data with covariates
• A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data
• Estimation in a linear regression model with censored data
• Asymptotic theory for the semiparametric accelerated failure time model with missing data
• Estimating regression parameters using linear rank tests for censored data
• Sieve maximum likelihood estimation for doubly semiparametric zero-inflated Poisson models
• Large sample theory of a modified Buckley-James estimator for regression analysis with censored data
• Convergence rate of sieve estimates
• On methods of sieves and penalization
• On parameters of increasing dimensions
• Efficient estimation of the partly linear additive Cox model
• Efficient estimation for the proportional hazards model with interval censoring
• Asymptotic efficiency in estimation with conditional moment restrictions
• Weak convergence and empirical processes. With applications to statistics
• A large sample study of rank estimation for censored regression data
• A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval-Censored Data
• Efficient Estimation for the Accelerated Failure Time Model
• Linear regression with censored data
• Regression with censored data
• Linear rank tests with right censored data
• Asymptotic Statistics
• Rank-based inference for the accelerated failure time model
• Estimation of Semiparametric Models when the Criterion Function Is Not Smooth
• Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
• On least-squares regression with censored data

This page was built for publication: A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data