Robust estimation based on one-shot device test data under log-normal lifetimes

Castilla, Elena

doi:10.1080/02331888.2023.2240925

Search citation statements

Order By: Relevance

Paper Sections

Select...

Model Formulation1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most studied ones in the context of one‐shot device testing are, probably, exponential 2,9,11 , gamma 3,12 , and Weibull distributions 4,7 . However, other different distributions have also been considered in the context of one‐shot device testing, such as generalized gamma 13 , log‐normal 5,14 , Birnbaum‐Saunders 15 and logistic‐exponential 16 . For the sake of illustration, we present here the following lifetime distributions.…”

Section: Model Formulationmentioning

confidence: 99%

A new estimation approach based on phi‐divergence measures for one‐shot device accelerated life testing

Castilla

2024

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

One‐shot device testing data are used only once and they get destroyed when tested. As these products usually have large mean times to failure under normal operating conditions, accelerated life tests are commonly used to infer their lifetime distribution. While much work has been done to determine the maximum likelihood estimates (MLEs) of model parameters for one‐shot device accelerated life testing, the efficiency of these methods may not be guaranteed for small to moderate sample sizes. In this paper, we develop new estimators and confidence intervals based on phi‐divergences, we show that they outperform the conventional MLE under different lifetime distributions and present an example to illustrate all the inferential methods developed here.

show abstract