Geodesic
Bias of LS estimators in nonlinear regression models with constraints. Part II: Biadditive models
Applications of Mathematics, Tome 44 (1999) no. 5, pp. 375-403

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General results giving approximate bias for nonlinear models with constrained parameters are applied to bilinear models in anova framework, called biadditive models. Known results on the information matrix and the asymptotic variance matrix of the parameters are summarized, and the Jacobians and Hessians of the response and of the constraints are derived. These intermediate results are the basis for any subsequent second order study of the model. Despite the large number of parameters involved, bias formulæ turn out to be quite simple due to the orthogonal structure of the model. In particular, the response estimators are shown to be approximately unbiased. Some simulations assess the validity of the approximations.
DOI : 10.1023/A:1023045028073
Classification : 62F12, 62F30, 62J02
Keywords: asymptotic variance; bilinear model; nonlinear least squares; response function; second order approximation 
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     title = {Bias of {LS} estimators in nonlinear regression models with constraints. {Part} {II:} {Biadditive} models},
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Denis, Jean-Baptiste; Pázman, Andrej. Bias of LS estimators in nonlinear regression models with constraints. Part II: Biadditive models. Applications of Mathematics, Tome 44 (1999) no. 5, pp. 375-403. doi: 10.1023/A:1023045028073

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