Robust Quantile Analysis for Accelerated Life Test Data

We propose a quantile regression framework to model accelerated life tests (ALT) data. The quantile of the failure time distribution at the usage level can be easily estimated using quantile regression. Compared with traditional parametric regression methods, quantile regression is distribution-free, efficient in the presence of censoring, and more flexible in modeling ALT relations. More importantly, we show that it is able to handle ALT data with a failure-free life, which is a great challenge in the ALT literature. We use extensive simulation studies and two real ALT case studies to demonstrate the effectiveness of the proposed method.

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