Nonparametric estimation of bivariate survivor function under masked causes of failure

Abstract

Consider a system that consists of k components. Each component is subject to more than one cause of failure. Due to inadequacy in the diagnostic mechanism or reluctance to report any specific cause of failure (disease), the exact cause of failure cannot be identified easily. In such situations, where the cause of failure is masked, test procedures restrict the cause of failure to a set of possible types containing the true failure cause. In this paper, we develop a nonparametric estimator for the bivariate survivor function of competing risk models under masked causes of failure based on the vector hazard rate. Asymptotic properties of the estimator are established. A simulation study is carried out to assess the performance of the estimator. We also illustrate the method with a data set.