Kernel multivariable semiparametric regression model in estimating the level of open unemption in East Java Province

Andi Tenri Ampa; I. Nyoman Budiantara; Ismaini Zain

doi:10.1088/1742-6596/1899/1/012127

Kernel multivariable semiparametric regression model in estimating the level of open unemption in East Java Province

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

^*Corresponding author for this work

Haluoleo University

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

Abstract

Semiparametric regression is a combination of parametric regression and nonparametric regression. Parametric regression curve components are approximated by multivariable linear functions, nonparametric regression curve components are approximated by Gaussian Kernel function. The purpose of this study is to obtain an estimate of the shape in semiparametric regression using the Kernel estimator and to model the Open Unemployment Rate (TPT) in East Java Province using the semiparametric regression model. This semiparametric regression model estimates on bandwidth. Semiparametric regression model is obtained by minimizing the Generalized Cross Validation function. The semiparametric regression model used to model TPT case data in East Java Province.

Original language	English
Article number	012127
Journal	Journal of Physics: Conference Series
Volume	1899
Issue number	1
DOIs	https://doi.org/10.1088/1742-6596/1899/1/012127
Publication status	Published - 28 May 2021
Event	2nd Workshop on Engineering, Education, Applied Sciences and Technology, WEAST 2020 - Makassar, Indonesia Duration: 5 Oct 2020 → …

Keywords

Generalized Cross Validation
Kernel
Semiparametric Regression

UN SDGs

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

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10.1088/1742-6596/1899/1/012127

Cite this

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title = "Kernel multivariable semiparametric regression model in estimating the level of open unemption in East Java Province",

abstract = "Semiparametric regression is a combination of parametric regression and nonparametric regression. Parametric regression curve components are approximated by multivariable linear functions, nonparametric regression curve components are approximated by Gaussian Kernel function. The purpose of this study is to obtain an estimate of the shape in semiparametric regression using the Kernel estimator and to model the Open Unemployment Rate (TPT) in East Java Province using the semiparametric regression model. This semiparametric regression model estimates on bandwidth. Semiparametric regression model is obtained by minimizing the Generalized Cross Validation function. The semiparametric regression model used to model TPT case data in East Java Province.",

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AU - Ampa, Andi Tenri

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AU - Zain, Ismaini

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N2 - Semiparametric regression is a combination of parametric regression and nonparametric regression. Parametric regression curve components are approximated by multivariable linear functions, nonparametric regression curve components are approximated by Gaussian Kernel function. The purpose of this study is to obtain an estimate of the shape in semiparametric regression using the Kernel estimator and to model the Open Unemployment Rate (TPT) in East Java Province using the semiparametric regression model. This semiparametric regression model estimates on bandwidth. Semiparametric regression model is obtained by minimizing the Generalized Cross Validation function. The semiparametric regression model used to model TPT case data in East Java Province.

AB - Semiparametric regression is a combination of parametric regression and nonparametric regression. Parametric regression curve components are approximated by multivariable linear functions, nonparametric regression curve components are approximated by Gaussian Kernel function. The purpose of this study is to obtain an estimate of the shape in semiparametric regression using the Kernel estimator and to model the Open Unemployment Rate (TPT) in East Java Province using the semiparametric regression model. This semiparametric regression model estimates on bandwidth. Semiparametric regression model is obtained by minimizing the Generalized Cross Validation function. The semiparametric regression model used to model TPT case data in East Java Province.

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KW - Kernel

KW - Semiparametric Regression

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JF - Journal of Physics: Conference Series

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Kernel multivariable semiparametric regression model in estimating the level of open unemption in East Java Province

Abstract

Keywords

UN SDGs

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