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The Modified Gauss-Newton Method for the Fitting of Non-Linear Regression Functions by Least Squares
Abstract
The experimental scientist is frequently faced with the task of determining the functional relation between a 'response', y and a number of 'inputs', x , x2, . * * , x, with the help of empirical data. Often the mathematical form of the functional relation is assumed to be known and is written in the form of a 'regression function',
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Citations
Cited by
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Gobel, F. L., Norstrom, L. A., Nelson, R. R., Jorgensen, C. R., & Wang, Y. (1978) · Circulation · doi:10.1161/01.cir.57.3.549 · Cited by 866
[16] Nonlinear least-squares analysis
Johnson, M. L., & Frasier, S. G. (1985) · Methods in Enzymology · doi:10.1016/s0076-6879(85)17018-7 · Cited by 451
Human disposition of L-theanine in tea or aqueous solution
van der Pijl, P. C., Chen, L., & Mulder, T. P. J. (2010) · Journal of Functional Foods · doi:10.1016/j.jff.2010.08.001 · Cited by 66
Metadata
- Authors
- Hartley, H. O.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1961-05
- Volume
- 3
- Issue
- 2
- Pages
- 269-280
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W1966523582
- Cited by
- 845
- References
- 0
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Modified Gauss-Newton Method for the Fitting of Non-Linear Regression Functions by Least Squares. https://app.cochat.ai/papers/10.1080/00401706.1961.10489945
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- Hartley, H. O. (1961). The Modified Gauss-Newton Method for the Fitting of Non-Linear Regression Functions by Least Squares. Technometrics. https://doi.org/10.1080/00401706.1961.10489945
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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.
