Robust copula estimation for one-shot devices with correlated failure modes

TL;DR

This paper introduces a divergence-based robust estimation method for copula models to accurately assess dependence between failure modes in one-shot devices, especially under outliers or model misspecification.

Contribution

It proposes a novel divergence-based estimation technique that improves robustness over traditional MLE for copula models in one-shot device failure analysis.

Findings

Simulation studies confirm enhanced robustness of the method.

Application to real-world data demonstrates practical utility.

Abstract

This paper presents a robust method for estimating copula models to evaluate dependence between failure modes in one-shot devices-systems designed for single use and destroyed upon activation. Traditional approaches, such as maximum likelihood estimation (MLE), often produce unreliable results when faced with outliers or model misspecification. To overcome these limitations, we introduce a divergence-based estimation technique that enhances robustness and provides a more reliable characterization of the joint failure-time distribution. Extensive simulation studies confirm the robustness of the proposed method. Additionally, we illustrate its practical utility through the analysis of a real-world dataset.