{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:12:44Z","timestamp":1726186364110},"reference-count":34,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2018,3,13]],"date-time":"2018-03-13T00:00:00Z","timestamp":1520899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quality & Reliability Eng"],"published-print":{"date-parts":[[2018,7]]},"abstract":"Abstract<\/jats:title>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\u2013based RPD models that focus on survival times and hazard rate. Primary tools used in this paper include the Kaplan\u2010Meier 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\u2013based RPD optimization models are proposed, and their RPD solutions are compared.<\/jats:p>","DOI":"10.1002\/qre.2283","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T10:48:48Z","timestamp":1521110928000},"page":"731-747","source":"Crossref","is-referenced-by-count":3,"title":["Robust parameter design optimization for type\u2010I<\/scp>right censored data"],"prefix":"10.1002","volume":"34","author":[{"given":"Anintaya","family":"Khamkanya","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering Thammasat University Bangkok Thailand"}]},{"given":"Byung Rae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering Clemson University Clemson SC USA"}]},{"given":"Tugce","family":"Isik","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering Clemson University Clemson SC 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