A simple procedure for testing linear hypotheses about the parameters of a nonlinear model using weighted least squares
作者:
Paulette Johnson,
George A. Milliken,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1983)
卷期:
Volume 12,
issue 2
页码: 135-145
ISSN:0361-0918
年代: 1983
DOI:10.1080/03610918308812307
出版商: Marcel Dekker, Inc.
关键词: Jacobian;cross-classified design;covariance analysis;reparameterization;growth model
数据来源: Taylor
摘要:
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.
点击下载:
PDF (303KB)
返 回