Average regression surface for dependent data

[摘要] We study the estimation of the additive components in additive regression models, based on the weighted sample average of regression surface, for stationary alpha -mixing processes. Explicit expression of this method makes possible a last computation and allows an asymptotic analysis. The estimation procedure is especially useful for additive modeling. In this paper, it is shown that the average surface estimator shares the same optimality as the ideal estimator and has the same ability to estimate the additive component as the ideal case where other components are known. Formulas for the asymptotic bias and normality of the estimator are established. A small simulation study is carried out to illustrate the performance of the estimation and a real example is also used to demonstrate our methodology. (C) 2000 Academic Press.

[发布日期] 2000-10-01

[关键词] additive models;alpha-mixing;asymptotic bias;asymptotic normality;local linear estimate;kernel estimates