چكيده به لاتين
In this thesis, analysis of failure time and right censoring time which considered Missing at Random was performed using a simulation study. If there exists an independence between the failure time and censoring time, then the Product-Limit estimator of Kaplan-Meier will be applied for estimation of the survival function but in the case of dependence between the two aforementioned variables, the consistency of Kaplan-Meier estimator decreases, therefore it can’t be an appropriate estimator for dealing with the dependent case. For this reason, a Copula-graphic estimator was proposed for the estimation of survival function. Considering the existence of missing at random data, Regression Surrogate method, Imputation method, and Inverse Probability Weighting method are introduced for the estimation of survival function, and their performances are compared to the existing estimator in this literature, and the best performance is investigated through a simulation study. The results of this simulation show that this copula function is appropriate enough for modeling and its survival curves were close to the true survival function. In addition, in the case of misspecification of the copula function, the results of the proposed estimators were better than other estimators.
