Brimacombe, M. (2016) A Note on Linear and Second Order Significance Testing in Nonlinear Models. International Journal of Statistics and Probability, 5, 19-27.

has been cited by the following article:

• TITLE: Local Curvature and Centering Effects in Nonlinear Regression Models

  AUTHORS: Michael Brimacombe

  KEYWORDS: Nonlinear Regression, Centering Data, Model Mis-Specification, Bias, Curvature

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.1, February 23, 2016

  ABSTRACT: The effects of centering response and explanatory variables as a way of simplifying fitted linear models in the presence of correlation are reviewed and extended to include nonlinear models, common in many biological and economic applications. In a nonlinear model, the use of a local approximation can modify the effect of centering. Even in the presence of uncorrelated explanatory variables, centering may affect linear approximations and related test statistics. An approach to assessing this effect in relation to intrinsic curvature is developed and applied. Mis-specification bias of linear versus nonlinear models also reflects this centering effect.