= Identifiability of additive regression components
{title2=$\sum_if_j(x_{ij})=0$}
In an <additive regression model>, each component can transfer a constant to the <regression intercept>, so impose empirical mean-zero constraints, or population mean-zero constraints when appropriate. Even after centering, nonzero component perturbations $h_j$ with $\sum_jh_j(x_{ij})=0$ at every observed point make the additive decomposition nonidentifiable. Extra smoothing penalties may resolve the fitted decomposition, but centering alone does not remove <concurvity>.
// Target: probability-and-statistics.bigb
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