The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Response surface methodology
Abstract
The purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951–1975, during which the so‐called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976–1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under:Statistical Models > Linear Models Statistical Models > Nonlinear Models
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Cites
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-27255-0_9 · Cited by 20,133
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Gunst, R. F., Myers, R. H., & Montgomery, D. C. (1996) · Technometrics · doi:10.2307/1270613 · Cited by 9,530
An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
Zellner, A. (1962) · Journal of the American Statistical Association · doi:10.1080/01621459.1962.10480664 · Cited by 8,401
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344614 · Cited by 7,194
Addelman, S. (1979) · Technometrics · doi:10.1080/00401706.1979.10489788 · Cited by 6,750
Multivariate Statistical Methods
(2008) · Encyclopedia of Public Health · doi:10.1007/978-1-4020-5614-7_2263 · Cited by 5,580
Simultaneous Optimization of Several Response Variables
Derringer, G., & Suich, R. (1980) · Journal of Quality Technology · doi:10.1080/00224065.1980.11980968 · Cited by 5,212
Generalized Linear Models (2nd ed.).
Therneau, T. M., McCullagh, P., & Nelder, J. A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290358 · Cited by 4,907
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552
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Alara, O. R., Abdurahman, N. H., Ukaegbu, C. I., & Azhari, N. H. (2018) · Industrial Crops and Products · doi:10.1016/j.indcrop.2018.06.034 · Cited by 176
Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5)
Ramesh, S., Karunamoorthy, L., & Palanikumar, K. (2012) · Measurement · doi:10.1016/j.measurement.2012.01.010 · Cited by 151
Golmakani, M.-T., Mendiola, J. A., Rezaei, K., & Ibáñez, E. (2012) · The Journal of Supercritical Fluids · doi:10.1016/j.supflu.2011.11.026 · Cited by 101
Relating alternative forms of contingency fit to the appropriate methods to test them
Burkert, M., Davila, A., Mehta, K., & Oyon, D. (2014) · Management Accounting Research · doi:10.1016/j.mar.2013.07.008 · Cited by 87
Kumari, S., Agarwal, S., Kumar, M., Sharma, P., Kumar, A., Hashem, A., Alotaibi, N. H., Abd-Allah, E. F., & Garg, M. C. (2025) · Scientific Reports · doi:10.1038/s41598-025-87274-3 · Cited by 78
Metadata
- Authors
- Khuri, A. I., & Mukhopadhyay, S.
- Venue
- WIREs Computational Statistics
- Publisher
- Wiley
- Published
- 2010-03
- Volume
- 2
- Issue
- 2
- Pages
- 128-149
- ISSN
- 1939-0068 (ISSN-L)
- 1939-5108
- OpenAlex
- W2107845890
- Cited by
- 2,113
- References
- 76
- FWCI
- 48.40
- Type
- journal article
- Language
- en
- Open access
- closed
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