In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in the presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where we often evidence that inflicted with a certain disease, survival time would be under different stress factors like environmental stress, co-morbidity, the severity of disease etc. This work is concerned with a one-shot device data analysis and applies it to SEER Gallbladder cancer data. The two-parameter logistic exponential distribution is applied as a lifetime distribution. For robust parameter estimation, weighted minimum density power divergence estimators (WMDPDE) is obtained along with the conventional maximum likelihood estimators (MLE). The asymptotic behaviour of the WMDPDE and the robust test statistic based on the density power divergence measure are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to SEER Gallbladder cancer data. Citing the importance of knowing exactly when to inspect the one-shot devices put to the test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is accomplished through the population-based heuristic optimization method Genetic Algorithm.

翻译：文献中，单次检测设备的可靠性分析通常在加速寿命试验环境下考虑多种应力因素。单次检测设备的应用可扩展至生物医学领域，在该领域中常观察到：罹患特定疾病后，生存时间会受环境压力、合并症、疾病严重程度等不同应力因素影响。本研究针对单次检测设备数据分析问题，并将其应用于SEER胆囊癌数据。采用双参数logistic指数分布作为寿命分布。为获得稳健参数估计，在传统极大似然估计（MLE）基础上，本文引入了基于密度幂散度的加权最小密度幂散度估计量（WMDPDE）。同时研究了WMDPDE的渐近性质以及基于密度幂散度测度的稳健检验统计量。通过大量仿真实验评估了各估计量的性能，并将上述方法应用于SEER胆囊癌数据。针对单次检测设备测试中确定最佳检测时机的重要性，本文开展了最优检测时间搜索研究。该优化旨在最小化定义的成本函数，该函数在估计精度与实验成本之间取得平衡。采用基于种群的启发式优化算法——遗传算法实现该搜索过程。