The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Response Surface Methodology: A Retrospective and Literature Survey
Abstract
Response surface methodology (RSM) is a collection of statistical design and numerical optimization techniques used to optimize processes and product designs. The original work in this area dates from the 1950s and has been widely used, especially in the chemical and process industries. The last 15 years have seen the widespread application of RSM and many new developments. In this review paper we focus on RSM activities since 1989. We discuss current areas of research and mention some areas for future research.
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Cites
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Minimax and maximin distance designs
Johnson, M. E., Moore, L. M., & Ylvisaker, D. (1990) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(90)90122-b · Cited by 1,451
Designing fractional two-level experiments with nested error structures
Schoen, E. D. (1999) · Journal of Applied Statistics · doi:10.1080/02664769922377 · Cited by 29
Cited by
Khuri, A. I., & Mukhopadhyay, S. (2010) · WIREs Computational Statistics · doi:10.1002/wics.73 · Cited by 2,113
Response-Surface Methods in R, Using rsm
Lenth, R. V. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i07 · Cited by 668
Metadata
- Authors
- Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., & Kowalski, S. M.
- Venue
- Journal of Quality Technology
- Publisher
- Informa UK Limited
- Published
- 2004-01
- Volume
- 36
- Issue
- 1
- Pages
- 53-77
- ISSN
- 0022-4065 (ISSN-L)
- 2575-6230
- OpenAlex
- W159436435
- Cited by
- 704
- References
- 3
- FWCI
- 19.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., & Kowalski, S. M. (2004). Response Surface Methodology: A Retrospective and Literature Survey. Journal of Quality Technology. https://doi.org/10.1080/00224065.2004.11980252
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
