Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

Andi Tenri Ampa; I. Nyoman Budiantara; Ismaini Zain

doi:10.1088/1742-6596/2123/1/012035

Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

Andi Tenri Ampa, I. Nyoman Budiantara, Ismaini Zain^*

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

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

Abstract

In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.

Original language	English
Article number	012035
Journal	Journal of Physics: Conference Series
Volume	2123
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/2123/1/012035
Publication status	Published - 7 Dec 2021
Event	4th International Conference on Statistics, Mathematics, Teaching, and Research, ICSMTR 2021 - Makassar, Indonesia Duration: 9 Oct 2021 → 10 Oct 2021

Keywords

Fourier series
GCV
Kernel
Smoothing parameters

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10.1088/1742-6596/2123/1/012035

Cite this

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title = "Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression",

abstract = "In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.",

keywords = "Fourier series, GCV, Kernel, Smoothing parameters",

author = "Ampa, {Andi Tenri} and Budiantara, {I. Nyoman} and Ismaini Zain",

note = "Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 4th International Conference on Statistics, Mathematics, Teaching, and Research, ICSMTR 2021 ; Conference date: 09-10-2021 Through 10-10-2021",

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doi = "10.1088/1742-6596/2123/1/012035",

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TY - JOUR

T1 - Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

AU - Ampa, Andi Tenri

AU - Budiantara, I. Nyoman

AU - Zain, Ismaini

PY - 2021/12/7

Y1 - 2021/12/7

N2 - In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.

AB - In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.

KW - Fourier series

KW - GCV

KW - Kernel

KW - Smoothing parameters

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DO - 10.1088/1742-6596/2123/1/012035

M3 - Conference article

AN - SCOPUS:85122443246

SN - 1742-6588

VL - 2123

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012035

T2 - 4th International Conference on Statistics, Mathematics, Teaching, and Research, ICSMTR 2021

Y2 - 9 October 2021 through 10 October 2021

ER -

Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

Abstract

Keywords

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