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.

Original languageEnglish
Pages (from-to)6083-6094
Number of pages12
JournalApplied Mathematical Sciences
Volume9
Issue number122
DOIs
Publication statusPublished - 2015

Keywords

  • GCV
  • Kernel
  • Mixed Estimator
  • Nonparametric Regression
  • Spline

Fingerprint

Dive into the research topics of 'The Combination of Spline and Kernel Estimator for Nonparametric Regression and its Properties'. Together they form a unique fingerprint.

Cite this