Divergence-Based Robust Inference Under Proportional Hazards Model for One-Shot Device Life-Test

被引：9

作者：

Balakrishnan, Narayanaswamy ^{[1
]}

Castilla, Elena ^{[2
,3
]}

Martin, Nirian ^{[4
]}

Pardo, Leandro ^{[3
]}

机构：

[1] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S4K1, Canada

[2] Univ Complutense Madrid, Dept Stat & Operat Res, Madrid 28040, Spain

[3] Inst Matemat Interdisciplinar IMI, Madrid, Spain

[4] Univ Complutense Madrid, Dept Financial & Actuarial Econ & Stat, Madrid 28040, Spain

来源：

IEEE TRANSACTIONS ON RELIABILITY | 2021年 / 70卷 / 04期

关键词：

Hazards; Stress; Reliability; Maximum likelihood estimation; Inspection; Robustness; Mathematical model; Minimum density power divergence estimator; one-shot devices; proportional hazards model; robustness; Wald-type tests;

D O I：

10.1109/TR.2021.3062289

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, we develop robust estimators and tests for one-shot device testing under proportional hazards assumption based on divergence measures. Through a detailed Monte-Carlo simulation study and a numerical example, the developed inferential procedures are shown to be more robust against data contamination than the classical procedures, based on maximum likelihood estimators.

引用

页码：1355 / 1367

页数：13

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