Estimation in the Semiparametric Frailty Model with Covariate Measurement Errors

Author

Huanbin, Liu ; Liuquan, Sun

fYear

2009

fDate

11-12 July 2009

Firstpage

546

Lastpage

549

Abstract

Marginal partial likelihood approach is used for estimating the parameters in general frailty measurement error models when covariates are measured with error. An efficient algorithm based on Markov chain Monte Carlo stochastic approximation is proposed to solve the resulting estimating equations. Simulation studies show that the proposed estimation procedure work well and gives accurate estimates and their variance estimates. We also illustrate the method with a data set from the western Kenya parasitaemia data.

Keywords

Markov processes; Monte Carlo methods; covariance analysis; measurement errors; parameter estimation; Markov chain Monte Carlo stochastic approximation; covariate measurement error; estimation procedure; general frailty measurement error model; marginal partial likelihood approach; parameter estimation; semiparametric frailty model; Approximation algorithms; Hazards; Laplace equations; Mathematical model; Mathematics; Maximum likelihood estimation; Measurement errors; Monte Carlo methods; Parameter estimation; Stochastic processes; Censored Survival data; Frailty models; General parameter families; Markov chain Monte Carlo methods; Stochastic approximation; covariate measurement errors;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-0-7695-3728-3

Type

conf

DOI

10.1109/CASE.2009.57

Filename

5194512