Document Type
Dissertation
Date of Award
Spring 5-31-2018
Degree Name
Doctor of Philosophy in Mathematical Sciences - (Ph.D.)
Department
Mathematical Sciences
First Advisor
Antai Wang
Second Advisor
Sunil Kumar Dhar
Third Advisor
Ji Meng Loh
Fourth Advisor
Sundarraman Subramanian
Fifth Advisor
Zhi Wei
Abstract
This dissertation has three independent parts. The first part studies a variation of the competing risks problem, known as the semi-competing risks problem, in which a terminal event censors a non-terminal event, but not vice versa, in the presence of a censoring event which is independent of these two events. The joint distribution of the two dependent events is formulated under Archimedean copula. An estimator for the association parameter of the copula is proposed, which is shown to be consistent. Simulation shows that the method works well with most common Archimedean copula models.
The second part studies the properties of a special class of frailty models when the frailty is common to several failure times. The model is closely linked to Archimedean copula models. A useful formula for baseline hazard functions for this class of frailty models is established. A new estimator for baseline hazard functions in bivariate frailty models based on dependent censored data with covariates is obtained, and a model checking procedure is presented.
The third part studies the properties of frailty models for bivariate data under fixed left censoring. It turns out that the distribution of observable pairs belongs to a new class of bivariate frailty models. Both the original model for complete data and the new model for observable pairs are members of Archimedean copula family. A new estimation strategy to analyze left-censored data using the corresponding Kendalls distribution is established.
Recommended Citation
Jia, Xieyang, "Survival analysis using archimedean copulas" (2018). Dissertations. 1369.
https://digitalcommons.njit.edu/dissertations/1369
