Chapter 10 Robust inference under the exponential distribution and competing risks ⍟ ⍟ This book has a companion website hosting complementary materials. Visit this URL to access it: https://data.mendeley.com/datasets/879xmdz3d8/1.

In this chapter, we introduce a family of robust estimators and Wald-type tests based on the density power divergence (DPD) measure for one-shot device testing under constant-stress accelerated life tests (CSALTs), with competing risks and lifetimes following the exponential distribution. These DPD-based estimators and Wald-type tests include the maximum likelihood estimator (MLE) and the classical Wald test, respectively, as a particular case. We prove the robustness of the proposed procedures through an extensive simulation study. We conclude the chapter with a numerical example on mice tumor data, illustrating the practical utility of the methods developed here.