Semiparametric inference for censored data and its applications

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In clinical trials, estimation of the treatment effect relies on some assumptions for statistical models. Violation of the assumptions may lead to difficulty to interpret results of the statistical analysis. In this research, we propose a method, which is interpretable in the presence of the violation. In addition, it is desirable to incorporate covariate information to draw more precise inference. We develop a method efficiently utilizing covariate information to increase statistical precision.