Efficient estimation of semiparametric copula models for bivariate survival data

Handle URI:
http://hdl.handle.net/10754/598105
Title:
Efficient estimation of semiparametric copula models for bivariate survival data
Authors:
Cheng, Guang; Zhou, Lan; Chen, Xiaohong; Huang, Jianhua Z.
Abstract:
A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.
Citation:
Cheng G, Zhou L, Chen X, Huang JZ (2014) Efficient estimation of semiparametric copula models for bivariate survival data. Journal of Multivariate Analysis 123: 330–344. Available: http://dx.doi.org/10.1016/j.jmva.2013.10.008.
Publisher:
Elsevier BV
Journal:
Journal of Multivariate Analysis
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Jan-2014
DOI:
10.1016/j.jmva.2013.10.008
Type:
Article
ISSN:
0047-259X
Sponsors:
Chen's research was partially sponsored by NSF (SES-0838161). Cheng's research was sponsored by NSF (DMS-0906497 and CAREER Award DMS-1151692). Huang's research was partly sponsored by NSF (DMS-0907170, DMS-1007618), and Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). Zhou's research was partially sponsored by NSF (DMS-0907170). The authors thank the editor, the associate editor, and one referee for insightful comments that led to important improvements in the paper.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorCheng, Guangen
dc.contributor.authorZhou, Lanen
dc.contributor.authorChen, Xiaohongen
dc.contributor.authorHuang, Jianhua Z.en
dc.date.accessioned2016-02-25T13:12:46Zen
dc.date.available2016-02-25T13:12:46Zen
dc.date.issued2014-01en
dc.identifier.citationCheng G, Zhou L, Chen X, Huang JZ (2014) Efficient estimation of semiparametric copula models for bivariate survival data. Journal of Multivariate Analysis 123: 330–344. Available: http://dx.doi.org/10.1016/j.jmva.2013.10.008.en
dc.identifier.issn0047-259Xen
dc.identifier.doi10.1016/j.jmva.2013.10.008en
dc.identifier.urihttp://hdl.handle.net/10754/598105en
dc.description.abstractA semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.en
dc.description.sponsorshipChen's research was partially sponsored by NSF (SES-0838161). Cheng's research was sponsored by NSF (DMS-0906497 and CAREER Award DMS-1151692). Huang's research was partly sponsored by NSF (DMS-0907170, DMS-1007618), and Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). Zhou's research was partially sponsored by NSF (DMS-0907170). The authors thank the editor, the associate editor, and one referee for insightful comments that led to important improvements in the paper.en
dc.publisherElsevier BVen
dc.subjectB-splineen
dc.subjectBivariate survival dataen
dc.subjectConsistent covariance estimationen
dc.subjectEfficiencyen
dc.subjectPrimaryen
dc.subjectSecondaryen
dc.subjectSemiparametric copula modelen
dc.titleEfficient estimation of semiparametric copula models for bivariate survival dataen
dc.typeArticleen
dc.identifier.journalJournal of Multivariate Analysisen
dc.contributor.institutionPurdue University, West Lafayette, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionYale University, New Haven, United Statesen
kaust.grant.numberKUS-CI-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.