A simple procedure for testing linear hypotheses about the parameters of a nonlinear model using weighted least squares |
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Authors: | Paulette Johnson George A. Milliken |
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Affiliation: | 1. Florida International University , Miami, Florida;2. Kansas State University , Manhattan, Kansas |
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Abstract: | Suppose the same nonlinear function involving k parameters is fit to each of t populations. Suppose further it is of interest to compare a specific parameter of the models across the populations. Such comparisons can be expressed as linear hypotheses about the parameters of the nonlinear models. A weighted linear least squares (WLLS) procedure is proposed to test these linear hypotheses. The advantages and disadvantages of the WLLS procedure are discussed. This procedure is also compared to a nonlinear least squares procedure for testing these hypotheses in nonlinear models. |
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Keywords: | Jacobian cross-classified design covariance analysis reparameterization growth model |
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