Generalized smooth models.

摘要

The class of gene linear models is extended to develop a class of nonparametric regression models known as generalized smooth models. The technique of local scoring is used to estimate a generalized smooth model and the estimation procedure based on locally weighted regression is shown to produce local likelihood estimates. The asymptotically correct distribution of the deviance difference is derived and its use in comparing the fits of generalized linear models and generalized smooth models is illustrated. The relationship between generalized smooth models and generalized additive models is discussed, also.