Kernel-Spline Estimation of Additive Nonparametric Regression Model

Rahmat Hidayat; I. Nyoman Budiantara; Bambang Widjanarko Otok; Vita Ratnasari

doi:10.1088/1757-899X/546/5/052028

Kernel-Spline Estimation of Additive Nonparametric Regression Model

Rahmat Hidayat
, I. Nyoman Budiantara^*
, Bambang Widjanarko Otok
, Vita Ratnasari

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Article number	052028
Journal	IOP Conference Series: Materials Science and Engineering
Volume	546
Issue number	5
DOIs	https://doi.org/10.1088/1757-899X/546/5/052028
Publication status	Published - 1 Jul 2019
Event	9th Annual Basic Science International Conference 2019, BaSIC 2019 - Malang, Indonesia Duration: 20 Mar 2019 → 21 Mar 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

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title = "Kernel-Spline Estimation of Additive Nonparametric Regression Model",

abstract = "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.",

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T1 - Kernel-Spline Estimation of Additive Nonparametric Regression Model

AU - Hidayat, Rahmat

AU - Budiantara, I. Nyoman

AU - Otok, Bambang Widjanarko

AU - Ratnasari, Vita

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

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U2 - 10.1088/1757-899X/546/5/052028

DO - 10.1088/1757-899X/546/5/052028

M3 - Conference article

AN - SCOPUS:85069518233

SN - 1757-8981

VL - 546

JO - IOP Conference Series: Materials Science and Engineering

JF - IOP Conference Series: Materials Science and Engineering

IS - 5

M1 - 052028

T2 - 9th Annual Basic Science International Conference 2019, BaSIC 2019

Y2 - 20 March 2019 through 21 March 2019

ER -

Kernel-Spline Estimation of Additive Nonparametric Regression Model

Abstract

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