Chapter 15 Backfitting Algorithm

The backfitting algorithm is applicable to any additive model (i.e. terms in the model are additive).

Example:

\[ y_i = f_1(x_{1i}) + f_2(x_{2i}) + \cdots + f_p(x_{pi}) + \varepsilon_i \] where \(f_k(.)\) is a smooth function (e.g. a linear function, a quadratic function, etc…).

15.1 Backfitting Algorithm for a Linear Model

Suppose the model is

\[ \textbf{y} = \textbf{x}_1\beta_1 + \textbf{x}_2\beta_2 + \cdots + \textbf{x}_p\beta_p +\boldsymbol{\varepsilon} \] where \(\mathbb{E}(\boldsymbol{\varepsilon})=\textbf{0}\) and \(Var(\boldsymbol{\varepsilon}) = \sigma^2\textbf{I}\)

References

Much of the content of this chapter is derived from materials of Asst.Prof. Michael Van Supranes.

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