Nonparametric Estimation of a Biometric Function Using Ranked Set Sampling With Ties Information

Abstract

The mean residual life (MRL) function plays an important role in the summary and analysis of survival data. The main advantage of this function is that it summarizes the information in units of time instead of a probability scale, which requires careful interpretation. Ranked set sampling (RSS) is a sampling technique designed for situations, where obtaining precise measurements of sample units is expensive or difficult, but ranking them without referring to their accurate values is cost-effective or easy. However, the practical application of RSS is hindered because each sample unit is required to assign a unique rank. To alleviate this difficulty, Frey developed a novel variation of RSS, called RSS-t, that records and utilizes the tie structure in the ranking process. In this paper, we propose several different nonparametric estimators for the MRL function based on RSS-t. Then, we compare the proposed estimators with their counterparts in simple random sampling (SRS) and RSS, where tie information is not utilized. We also implemented our proposed estimators on a real data set related to patient waiting times for liver transplantation, to show their applicability and efficiency in practice. Our results show that using ties information leads to an improved statistical inference for the MRL function, and therefore a smaller sample size is needed to reach a predetermined precision.