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Implicit Multifunctional Nonlinear Regression Analysis
William H. Sachs
Vol. 18, No. 2 (May, 1976), pp. 161-173
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1267519
Page Count: 13
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The least squares estimation of parameters in algebraically implicit, nonlinear, multiple response models having only one experimentally accessible response variable is treated within the context of a Gauss-Newton-Newton iteration. The algorithm, derived through application of the implicit function theorem to the model, is sufficiently general to cover Bayesian estimation of parameters for multiresponse data.
Technometrics © 1976 American Statistical Association