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Biometrika 1986 73(3):615-623; doi:10.1093/biomet/73.3.615
© 1986 by Biometrika Trust
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Bias in nonlinear regression

R. D. COOK, C.-L. TSAI and B. C. WEI

Department of Applied Statistics, University of Minnesota St. Paul, Minnesota 55108, U.S.A.
Department of Statistics and Operations Research, New York University New York, New York 10003, U.S.A.
Department of Mathematics, Nanjing Institute of Technology Jiangsu, People's Republic of China

We investigate the biases of the residuals and maximum likelihood parameter estimates from normal nonlinear regression models. Emphasis is placed on a class of partially nonlinear models, on the role of individual cases in determining bias, on how bias affects standard diagnostic methods, and on the relationship between bias and curvature.

Key Words: Diagnostic • Influence • Intrinsic curvature • Residual • Tarnsformation


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