The parametric generalized linear model assumes that the conditional distribution of a response Y given a d-dimensional covariate X belongs to an exponential family and that a known transformation of the regression function is linear in X. In this paper we relax the latter assumption by considering
Local Polynomial Fitting in Semivarying Coefficient Model
β Scribed by Wenyang Zhang; Sik-Yum Lee; Xinyuan Song
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 192 KB
- Volume
- 82
- Category
- Article
- ISSN
- 0047-259X
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β¦ Synopsis
Varying coefficient models are useful extensions of the classical linear models. Under the condition that the coefficient functions possess about the same degrees of smoothness, the model can easily be estimated via simple local regression. This leads to the one-step estimation procedure. In this paper, we consider a semivarying coefficient model which is an extension of the varying coefficient model, which is called the semivarying-coefficient model. Procedures for estimation of the linear part and the nonparametric part are developed and their associated statistical properties are studied. The proposed methods are illustrated by some simulation studies and a real example.
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