Nonparametric Survival Function for Pneumo Carcinoma Patients

Taha A. Taha, Sufyan Abdulraheem Mutar

Abstract: In this paper, we use a variety of nonparametric estimation techniques, based on a sample from complete real data, to estimate the survival function of lung cancer patients. This function describes the time a patient with lung cancer is expected to live after receiving a diagnosis of the disease or entering a hospital. The mean squared error was used to compare the aforementioned estimation techniques, and it was found that the shrinkage approach produced the best survival function for lung cancer.

Keywords: Non-parametric Estimation, Lung Carcinoid tumors Disease, Actual Data, Em-Estimator