Applied Mathematical Sciences, Vol. 9, 2015, no. 122, 6083 - 6094 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2015.58517 The Combination of Spline and Kernel Estimator for Nonparametric Regression and its Properties I Nyoman Budiantara, Vita Ratnasari, Madu Ratna and Ismaini Zain Statistics Department, Sepuluh Nopember Institute of Technology Kampus ITS, Sukolilo, Surabaya, Indonesia Copyright © 2015 I Nyoman Budiantara, Vita Ratnasari, Madu Ratna and Ismaini Zain. This article is distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Consider additive nonparametric regression model with two predictor variables components. In the first predictor component, the regression curve is approached using Spline regression, and in the second predictor component, the regression curve is approached using Kernel regression. Random error of regression model is assumed to have independent normal distribution with zero mean and the same variance. This article provides an estimator of Spline regression curve, estimator of Kernel regression curve, and an estimator of a combination of Spline and Kernel regressions. The produced estimators are biased estimators, but all estimators are classified as linear estimators in observation. Estimator of a combination of Spline and Kernel regression depended on knot points and bandwith parameter. The best estimator of a combination of Spline and Kernel regression is found by minimizing Generalized Cross Validation (GCV) function. Keywords: Nonparametric Regression, Spline, Kernel, Mixed Estimator, GCV 1. Introduction In recent decades, nonparametric regression has received a lot of attention from researchers. Nonparametric regression is a regression model approached used if the pattern of relation between predictor variable and response isn’t known, or if there is no complete past information on the shape of data pattern [1], [2], [3]. The nonparametric regression models which receive a lot of attention from re-