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Response Modeling Methodology Validating Evidence from Engineering and the Sciences
Author(s) -
Shore Haim
Publication year - 2004
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.547
Subject(s) - computer science , generalization , reliability (semiconductor) , econometrics , normalization (sociology) , goodness of fit , linear model , generalized linear model , linear regression , management science , industrial engineering , operations research , mathematics , machine learning , engineering , mathematical analysis , power (physics) , physics , quantum mechanics , sociology , anthropology
Modeling a response in terms of the factors that affect it is often required in quality applications. While the normal scenario is commonly assumed in such modeling efforts, leading to the application of linear regression analysis, there are cases when the assumptions underlying this scenario are not valid and alternative approaches need to be pursued, like the normalization of the data or generalized linear modeling. Recently, a new response modeling methodology (RMM) has been introduced, which seems to be a natural generalization of various current scientific and engineering mainstream models, where a monotone convex (concave) relationship between the response and the affecting factor (or a linear combination of factors) may be assumed. The purpose of this paper is to provide the quality practitioner with a survey of these models and demonstrate how they can be derived as special cases of the new RMM. A major implication of this survey is that RMM can be considered a valid approach for quality engineering modeling and, thus, may be conveniently applied where theory‐based models are not available or the goodness‐of‐fit of current empirically‐derived models is unsatisfactory. A numerical example demonstrates the application of the new RMM to software reliability‐growth modeling. The behavior of the new model when the systematic variation vanishes (there is only random variation) is also briefly explored. Copyright © 2003 John Wiley & Sons, Ltd.