= Additive regression model
{title2=$\mathbb E(Y\mid x)=\beta_0+\sum_jf_j(x_j)$}
An additive regression model represents a conditional mean as a sum of univariate functions and an intercept. It permits nonlinear effects without estimating a general high-dimensional response surface. The <backfitting algorithm> smooths each component's <partial residual>. Centering separates constants from the intercept, while <concurvity> can still prevent component identification.
// Target: probability-and-statistics.bigb
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