The regression curve estimation by using mixed smoothing spline and kernel (MsS-K) model
Rahmat Hidayat, I Nyoman Budiantara, Bambang Widjanarko Otok, Vita Ratnasari
Abstract
In this article, we propose a new method in estimating non parametric regression curve. This method combines the smoothing Spline and Kernel functions. Estimation of the estimator is completed by minimizing penalized least square. To see the performance of the model, this model is applied to simulation data with a variety of sample sizes and error variances. Then, the model is applied to the Unemployment Rate data in East Java Province, Indonesia. The results show that this model provides good performance in modeling data and predictions.
Topics & Concepts
Smoothing splineKernel smootherEstimatorMathematicsKernel regressionKernel (algebra)Spline (mechanical)SmoothingVariable kernel density estimationNonparametric regressionKernel density estimationStatisticsEstimationApplied mathematicsKernel methodComputer scienceArtificial intelligenceSpline interpolationRadial basis function kernelSupport vector machineCombinatoricsEngineeringStructural engineeringBilinear interpolationSystems engineeringAdvanced Statistical Methods and ModelsStatistical and numerical algorithmsImage and Signal Denoising Methods