Power Transformations and Reparameterizations in Nonlinear Regression Models
作者:
Chih-Ling Tsai,
期刊:
Technometrics
(Taylor Available online 1988)
卷期:
Volume 30,
issue 4
页码: 441-448
ISSN:0040-1706
年代: 1988
DOI:10.1080/00401706.1988.10488440
出版商: Taylor & Francis Group
关键词: Bias;Intrinsic curvature;Parameter-effects curvature;Score test
数据来源: Taylor
摘要:
A two-stage procedure is proposed to achieve normality and homoscedasticity of the classical error assumptions and to remove nonlinearity of the regression function. This systematic approach involves power transformations and reparameterization. Test statistics are obtained to assess the necessity of power transformations and the validity of homoscedasticity of the errors. In addition. nonlinearity measures are provided to diagnose the accuracy of linearization-based approximate confidence regions for parameters. Numerical illustrations of the two-stage procedure arc presented.
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