Open AccessKernel multivariable semiparametric regression model in estimating the level of open unemption in East Java Province
Author(s) -
Andi Tenri Ampa,
I Nyoman Budiantara,
Ismaini Zain
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1899/1/012127
Subject(s) - semiparametric regression , semiparametric model , nonparametric regression , regression analysis , statistics , kernel regression , mathematics , estimator , parametric statistics , polynomial regression , regression , econometrics
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.