Literature DB >> 8589230

Inferences on the association parameter in copula models for bivariate survival data.

J H Shih1, T A Louis.   

Abstract

We investigate two-stage parametric and two-stage semi-parametric estimation procedures for the association parameter in copula models for bivariate survival data where censoring in either or both components is allowed. We derive asymptotic properties of the estimators and compare their performance by simulations. Both parametric and semi-parametric estimators of the association parameter are efficient at independence, and the parameter estimates in the margins have high efficiency and are robust to misspecification of dependency structures. In addition, we propose a consistent variance estimator for the semi-parametric estimator of the association parameter. We apply the proposed methods to an AIDS data set for illustration.

Entities:  

Mesh:

Year:  1995        PMID: 8589230

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  62 in total

1.  A two-stage estimator of the dependence parameter for the Clayton-Oakes model.

Authors:  D V Glidden
Journal:  Lifetime Data Anal       Date:  2000-06       Impact factor: 1.588

2.  Estimating progression-free survival in paediatric brain tumour patients when some progression statuses are unknown.

Authors:  Ying Yuan; Peter F Thall; Johannes E Wolff
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2012-01-01       Impact factor: 1.864

3.  A two-stage estimation in the Clayton-Oakes model with marginal linear transformation models for multivariate failure time data.

Authors:  Chyong-Mei Chen; Chang-Yung Yu
Journal:  Lifetime Data Anal       Date:  2011-10-09       Impact factor: 1.588

4.  Non-parametric estimation of bivariate failure time associations in the presence of a competing risk.

Authors:  Karen Bandeen-Roche; Jing Ning
Journal:  Biometrika       Date:  2008-03-01       Impact factor: 2.445

5.  Copula based flexible modeling of associations between clustered event times.

Authors:  Candida Geerdens; Gerda Claeskens; Paul Janssen
Journal:  Lifetime Data Anal       Date:  2015-07-26       Impact factor: 1.588

6.  A Semi-stationary Copula Model Approach for Bivariate Survival Data with Interval Sampling.

Authors:  Hong Zhu; Mei-Cheng Wang
Journal:  Int J Biostat       Date:  2015-05       Impact factor: 0.968

7.  ROBUST MIXED EFFECTS MODEL FOR CLUSTERED FAILURE TIME DATA: APPLICATION TO HUNTINGTON'S DISEASE EVENT MEASURES.

Authors:  Tanya P Garcia; Yanyuan Ma; Karen Marder; Yuanjia Wang
Journal:  Ann Appl Stat       Date:  2017-07-20       Impact factor: 2.083

Review 8.  Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions.

Authors:  Jeongyong Kim; Karen Bandeen-Roche
Journal:  Lifetime Data Anal       Date:  2018-08-03       Impact factor: 1.588

9.  Modeling familial association of ages at onset of disease in the presence of competing risk.

Authors:  Joanna H Shih; Paul S Albert
Journal:  Biometrics       Date:  2010-12       Impact factor: 2.571

10.  Landmark risk prediction of residual life for breast cancer survival.

Authors:  Layla Parast; Tianxi Cai
Journal:  Stat Med       Date:  2013-03-14       Impact factor: 2.373

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.