Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative...

The concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlyingstructure. The book considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims,...

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between...