Improving estimates of critical lower percentiles by induced censoring

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...

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Bibliographic Details
Published in:Reliability engineering & system safety 2014-03, Vol.123, p.47-56
Main Authors: Edwards, David J., Guess, Frank M., León, Ramón V., Young, Timothy M., Crookston, Kevin A.
Format: Article
Language:English
Subjects:
Aging (artificial)
Aging period
Applied sciences
Bathtub curve
Bias
Estimates
Exact sciences and technology
Failure rates
Lower percentile estimation
Mean square values
Model misspecification
Operational research and scientific management
Operational research. Management science
Optimization
Reliability
Reliability theory. Replacement problems
Roots
Simulation
Weibull
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Summary: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.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2013.10.001