논문 상세정보
Robust parameter design optimization for type‐I right censored data
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초록
Abstract 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.
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주제어
censoring . Kaplan‐Meier estimator . proportional hazards . quality . response surface.
활용도 분석
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Issue Information
- A linear‐time method for computing the ANOS of Bernoulli and Poisson CUSUM charts
- EWMA chart based on Bayes‐conditional pivotal quantity for Weibull percentiles under complete data and type‐II censoring
- A global scheme for controlling the mean and standard deviation of a process based on the optimal design of gauges
- Robust parameter design optimization for type‐I right censored data
