Multivariable Semiparametric Regression Model with Combined Estimator of Fourier Series and Kernel

3rd International Seminar On Sciences "Sciences On Precision And Sustainable Agriculture"

Nisa, Khaerun^1 ; Budiantara, I Nyoman^1 ; Rumiati, Agnes Tuti^1

Department of Statistics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember (ITS), Arif Rahman Hakim Street, Surabaya

60111, Indonesia^1

关键词: Combined estimators;    Penalized least-squares;    Predictor variables;    Regression model;    Repetitive pattern;    Semi-parametric regressions;    Semiparametric regression model;    Smoothing parameter;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/58/1/012028/pdf
DOI  :  10.1088/1755-1315/58/1/012028

来源: IOP

【 摘 要 】

We purpose a combined estimator of Fourier series and Kernel on semiparametric regression. This method is used to resolve the problem of regression modeling, when the relationship between the response variable and the predictor variables most follow a certain pattern, partly have a repetitive pattern, and some others not follow a specific pattern. Moreover, this method depends on oscillation, smoothing parameter and bandwidth. The purpose of this research are to obtained the estimator of semiparametric regression model with combined estimator of Fourier series and Kernel using Penalized Least Square method (PLS). The result show that the PLS estimation produces the estimator of parametrik linier regression, the estimator of Fourier series, the estimator of Kernel, and also the combined estimator of Fourier series and Kernel in semiparametric regression model.