Bias of LS estimators in nonlinear regression models with constraints. Part II: Biadditive Models

Abstract

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 formulae 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.