Publication details
Additive models and kernel smoothing
| Authors | |
|---|---|
| Year of publication | 1999 |
| Type | Article in Proceedings |
| Conference | PROBASTAT'98 Proceedings of the Third International Conference on Mathematical Statistics |
| MU Faculty or unit | |
| Citation | |
| Field | General mathematics |
| Keywords | linear smoother; smoother matrices; additive model; orthogonal projection |
| Description | Nonparametric regression methods are often used to estimate an unknown function $m(x_1,\dots,x_p)$ in a regression model $$Y=m(X_1,\dots,X_p)+\eps$$ for random variables $X_1,\dots,X_p,Y$ and error $\eps$. Additive model can be used for the function $m$ in the special form $$m(x_1,\dots,x_p)=m_1(x_1)+\dots m_p(x_p).$$ Application of kernel smoothing to additive models is shown in this contribution and some practical results, too. |
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