Advanced SearchSearch Tips
Accuracy of Multiple Outlier Tests in Nonlinear Regression
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Accuracy of Multiple Outlier Tests in Nonlinear Regression
Kahng, Myung-Wook;
  PDF(new window)
The original Bates-Watts framework applies only to the complete parameter vector. Thus, guidelines developed in that framework can be misleading when the adequacy of the linear approximation is very different for different subsets. The subset curvature measures appear to be reliable indicators of the adequacy of linear approximation for an arbitrary subset of parameters in nonlinear models. Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. The accuracy of outlier tests is investigated using subset curvatures.
Curvature measures;intrinsic curvature;outlier test;parameter-effects curvature;subset curvatures;
 Cited by
Bates, D. M. and Watts, D. G. (1980). Relative curvature measures of nonlinearity (with discussion), Journal of the Royal Statistical Society Series, B, 42, 1-25.

Bates, D. M. and Watts, D. G. (1988). Nonlinear Regression Analysis and Its Applications, John Wiley and Sons, New York.

Cook, R. D. and Goldberg, M. L. (1986). Curvatures for parameter subsets in nonlinear regression, The Annals of Statistics, 14, 1399-1418. crossref(new window)

Cook, R. D. and Weisberg, S. (1990). Confidence curves in nonlinear regression, Journal of the American Statistical Association, 85, 544–551.

Kahng, M. (1995). Testing outliers in nonlinear regression, Journal of the Korean Statistical Society, 24, 419-437.

Kahng, M. and Kim, B. (2009). Assessing the accuracy of outlier tests in nonlinear regression, Communications of the Korean Statistical Society, 16, 163–168. crossref(new window)

Neyman, J. and Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference, Biometrika, 20A, 175-240 and 263-294.

Rao, C. R. (1947). Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation, Proceedings of the Cambridge Philosophical Society, 44, 50-57.

Ratkowsky, D. A. (1983). Nonlinear Regression Modeling: A Unified Practical Approach, Marcel Dekker, New York.

Silvey, S. D. (1959). The Lagrangian multiplier test, Annals of Mathematical Statistics, 30, 389–407.