… For this paper, we consider a survival analysis using Cox proportional hazards model, where
some values of the covariates of interest are randomly censored. Such censored covariate …

… survival data such as patients' overall survival or cancer relapse times. In the paper, we
develop a kernel Cox regression model for … in terms of function estimation in reproducing kernel …

… A Cox model is based on very small intervals of time, called time… For this reason, Cox
regression is used to model rates that … The Cox survival is valid when the proportional hazard …

… By performing the Cox regression analysis with T 1 and V 1j (one j at a time), we obtain
the maximized partial likelihood estimates and consequently get the second component …

… To illustrate the statistical techniques in fitting a Cox regression model, we consider a data
set from a cancer research study conducted by the International Bone Marrow Transplant …

… Estimate and Confidence Interval Before explaining the effect estimate of Cox regression
analysis… The effect estimate of Cox regression analyses is a hazard ratio (HR), this is the ratio of …

… comparison of Cox regression estimates derived by PLM, … estimator provides the better fit
to the data, and so it is especially appropriate for comparing conventional and robust estimates. …

… Gompertz model share the assumption of proportional hazards with the Cox regression model
[9]… A number of special distributions have been used in applications of the Cox model. In a …

… The objective of this study was to compare the performances of Cox regression analysis (CRA)
and random survival forests (RSF) methods with simulation and a real data set related to …

… estimated survival rates obtained from this analysis were higher. Since the correlation of
the log failure times of the different restorations from the same child was fairly strong (corrdentist …