A ranked-based estimator of the mean past lifetime with its application

TL;DR

This paper introduces a nonparametric, ranked-based estimator for the mean past lifetime in reliability analysis, demonstrating its consistency, efficiency, and application potential through simulations and an HIV case study.

Contribution

It develops a novel estimator for MPL using ranked set sampling, showing improved efficiency over simple random sampling under good ranking conditions.

Findings

The estimator is strongly uniformly consistent.

It converges to a Gaussian process under mild conditions.

It outperforms SRS-based estimators when ranking quality is high.

Abstract

The mean past lifetime (MPL) is an important tool in reliability and survival analysis for measuring the average time elapsed since the occurrence of an event, under the condition that the event has occurred before a specific time $t>0$. This article develops a nonparametric estimator for MPL based on observations collected according to ranked set sampling (RSS) design. It is shown that the estimator that we have developed is a strongly uniform consistent. It is also proved that the introduced estimator tends to a Gaussian process under some mild conditions. A Monte Carlo simulation study is employed to evaluate the performance of the proposed estimator with its competitor in simple random sampling (SRS). Our findings show the introduced estimator is more efficient than its counterpart estimator in SRS as long as the quality of ranking is better than random. Finally, an illustrative example is provided to describe the potential application of the developed estimator in assessing the average time between the infection and diagnosis in HIV patients.