Improving estimates of critical lower percentiles by induced censoring

Abstract

In this article, we present an approach based on induced censoring for improving the estimation of critical lower percentiles. We validate this technique via simulation results and practical industrial insights. Data from product components that have at least two aging periods (e.g., bathtub failure rate) is investigated. When such data are improperly fit by certain reliability distributions, estimates of lower percentiles are impacted by longer-lasting failures, resulting in larger root mean square errors (RMSE) and bias. In lieu of utilizing a more complex bathtub model, we propose induced right censoring of data at various points to substantially reduce RMSE and bias of lower percentile estimates. A technique for finding optimal or near optimal censoring points is discussed and two real world examples illustrate how this works in practice.