Semiparametric additive intensity model with frailty for recurrent events
type: Item
instance of: scholarly article
title: Semiparametric additive intensity model with frailty for recurrent events
zbMATH Open document ID: 1225.62054
author: Yanyan Liu
author: Yuan Shan Wu
publication date: 2011-11-04T00:00:00Z
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 62N02
zbMATH DE Number: 5968223
zbMATH Keywords: counting process
zbMATH Keywords: Donsker class
MaRDI profile type: Publication
full work available at URL: s10114-011-8232-x
OpenAlex ID: W2119560801
cites work: Cox's regression model for counting processes: A large sample study
cites work: Semiparametric analysis of the additive risk model
cites work: A partly parametric additive risk model
cites work: Q4012692
cites work: Consistency in a proportional hazards model incorporating a random effect
cites work: Asymptotic theory for the frailty model
cites work: Asymptotic theory for the correlated gamma-frailty model
cites work: Analysis of multivariate survival data
cites work: Semiparametric Transformation Models With Random Effects for Recurrent Events
cites work: An Estimating Equation for Parametric Shared Frailty Models with Marginal Additive Hazards
cites work: Statistical models based on counting processes
cites work: Q4951123
cites work: Q3999494
cites work: Survival analysis. Techniques for censored and truncated data.
cites work: Semiparametric analysis of general additive-multiplicative hazard models for counting processes
cites work: Additive-multiplicative rates model for recurrent events
cites work: On the Unique Consistent Solution to the Likelihood Equations
cites work: Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
cites work: Weak convergence and empirical processes. With applications to statistics
DOI: 10.1007/S10114-011-8232-X
published in: Acta Mathematica Sinica, English Series
description: scientific article; zbMATH DE number 5968223