Premium
Robust parameter design optimization for type‐ I right censored data
Author(s) -
Khamkanya Anintaya,
Cho Byung Rae,
Isik Tugce
Publication year - 2018
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2283
Subject(s) - censoring (clinical trials) , estimator , computer science , reliability (semiconductor) , accelerated failure time model , robustness (evolution) , sample size determination , proportional hazards model , mathematical optimization , statistics , mathematics , power (physics) , physics , biochemistry , chemistry , quantum mechanics , gene
Robust parameter design (RPD) aims to build product quality in the early design phase of product development by optimizing operating conditions of process parameters. A vast majority of the current RPD studies are based on an uncensored random sample from a process distribution. In reality, censoring schemes are widely implemented in lifetime testing, survival analysis, and reliability studies in which the value of a measurement is only partially known. However, there has been little work on the development of RPD when censored data are under study. To fill in the research gaps given practical needs, this paper proposes response surface–based RPD models that focus on survival times and hazard rate. Primary tools used in this paper include the Kaplan‐Meier estimator, Greenwood's formula, the Cox proportional hazards regression method, and a nonlinear programming method. The experimental modeling and optimization procedures are demonstrated through a numerical example. Various response surface–based RPD optimization models are proposed, and their RPD solutions are compared.