Robust Estimators and Test-Statistics for One-Shot Device Testing Under the Exponential Distribution

2017/04/25 by Balakrishnan, N., Castilla, E., Martin, N. +1 · 1 citation
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1704.07865

Abstract

This paper develops a new family of estimators, the minimum density power divergence estimators (MDPDEs), for the parameters of the one-shot device model as well as a new family of test statistics, Z-type test statistics based on MDPDEs, for testing the corresponding model parameters. The family of MDPDEs contains as a particular case the maximum likelihood estimator (MLE) considered in Balakrishnan and Ling (2012). Through a simulation study, it is shown that some MDPDEs have a better behavior than the MLE in relation to robustness. At the same time, it can be seen that some Z-type tests based on MDPDEs have a better behavior than the classical Z-test statistic also in terms of robustness.

Cited by

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

Related

• Divergence-based robust inference under proportional hazards model for one-shot device life-test
• Power divergence approach for one-shot device testing under competing risks
• Robust estimation based on one-shot device test data under log-normal lifetimes
• Robust statistical inference for accelerated life-tests with one-shot devices under log-logistic distributions
• The restricted minimum density power divergence estimator for non-destructive one-shot device testing the under step-stress model with exponential lifetimes