Open AccessKernel-Spline Estimation of Additive Nonparametric Regression Model
Author(s) -
Rahmat Hidayat,
I Nyoman Budiantara,
Bambang Widjanarko Otok,
Vita Ratnasari
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052028
Subject(s) - variable kernel density estimation , kernel smoother , kernel embedding of distributions , kernel (algebra) , mathematics , kernel regression , gaussian function , nonparametric regression , smoothing spline , kernel method , spline (mechanical) , statistics , computer science , radial basis function kernel , nonparametric statistics , gaussian , artificial intelligence , spline interpolation , engineering , combinatorics , physics , quantum mechanics , support vector machine , bilinear interpolation , structural engineering
In this paper, we model the open unemployment rate with the Kernel-Spline model. We investigate and compare the performance of model Kernel-Spline by varying the Kernel function. The performance model has been compared with five Kernel function i.e. Kernel functions Uniform, Epanechnikov, Quartic, Gaussian, and Triweight. For these models, we conducted a comparison based on actual data sets, the unemployment rate in East Java. The best model was chosen based on the Generalized Cross Validation value and the coefficient of determination criteria. The empirical results obtained have shown that Spline-Kernel model by using the Gaussian Kernel better than other models.