MOVING WINDOW ESTIMATION PROCEDURES FOR ADDITIVE REGRESSION FUNCTION

The general additive regression function b(x) = SIGMA b(j)(x(j)) is co nsidered and subjected to nonparametric estimation. The method of esti mation is inspired by the regressogram approximations to the component s of regression function. The procedure using the moving window is the n derived, it naturally generalizes to a kernel approach. The method c an be applied to the likelihood-based models, in which the value of re gression function is a parameter of likelihood of a response variable Y. Suggested moving window algorithm is a variant of Hastie and Tibshi rani's [3] local scoring procedure. In order to discuss the quality of obtained results, the method is compared with the approximation by re gression splines, treated successfully by Stone [6]. An example illust rates the solution for the logistic regression, the proportional hazar d regression model is also examined.