TY - JOUR
T1 - Efficient estimation of semiparametric copula models for bivariate survival data
AU - Cheng, Guang
AU - Zhou, Lan
AU - Chen, Xiaohong
AU - Huang, Jianhua Z.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/598105
UR - https://linkinghub.elsevier.com/retrieve/pii/S0047259X13002157
UR - http://www.scopus.com/inward/record.url?scp=84887232037&partnerID=8YFLogxK
U2 - 10.1016/j.jmva.2013.10.008
DO - 10.1016/j.jmva.2013.10.008
M3 - Article
SN - 0047-259X
VL - 123
SP - 330
EP - 344
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
ER -