Kernel-based estimation of P(X < Y) when X and Y are dependent random variables based on progressive type II censoring

Abstract

The most widely used approach for reliability estimation is the well-known stress-strength model, θ = P(X < Y), where X and Y are random variables. In this model, the reliability, θ, of the system is the probability that the system is strong enough to overcome the stress imposed on it. In most cases, X and Y are assumed to be independent. Nevertheless, in reality, the strength variable Y could be highly dependent on the stress variable X. In this paper, we discuss the kernel-based estimation of θ when X and Y are dependent random variables under progressive type II censored sample. The asymptotic properties of the kernel-based estimators of θ based on progressive type II censoring are proposed. An extensive computer simulation is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process.

Keywords:

• Dependence
• estimation
• kernel
• stress-strength model
• progressive type II censoring