TY - JOUR

T1 - Statistical inference on nonparametric regression model with approximation of Fourier series function

T2 - Estimation and hypothesis testing

AU - Ramli, Mustain

AU - Budiantara, I. Nyoman

AU - Ratnasari, Vita

N1 - Publisher Copyright:
© 2024 The Authors

PY - 2024/12

Y1 - 2024/12

N2 - Nonparametric regression is an approximation method in regression analysis that is not constrained by the assumption of knowing the regression curve. One of the functions to approximate the curve is a Fourier series function. The nonparametric regression model with approximation of a Fourier series function has been widely discussed by several researchers. However, discussions on statistical inference, particularly in partial hypothesis testing, has not been carried out previously. Therefore, the purpose of this research is to discuss the statistical inference on nonparametric regression model with approximation of a Fourier series function. The discussion includes parameter and model estimations, simultaneous and partial hypotheses testing. In the application, we use life expectancy data from East Java Province during 2022. Based on data analysis, we obtain a model estimation with an R-square value of 96.24 %. At a 5 % significance level, the parameters simultaneously have a significant influence on the model. Partially, four parameters are not significant. However, overall, the predictor variables significantly influence the life expectancy data. • The Fourier series function used is a Fourier series function introduced by Bilodeau (1992). • The model estimation is obtained by selecting the optimal number of oscillation parameters. • The statistical test is obtained using the LRT method.

AB - Nonparametric regression is an approximation method in regression analysis that is not constrained by the assumption of knowing the regression curve. One of the functions to approximate the curve is a Fourier series function. The nonparametric regression model with approximation of a Fourier series function has been widely discussed by several researchers. However, discussions on statistical inference, particularly in partial hypothesis testing, has not been carried out previously. Therefore, the purpose of this research is to discuss the statistical inference on nonparametric regression model with approximation of a Fourier series function. The discussion includes parameter and model estimations, simultaneous and partial hypotheses testing. In the application, we use life expectancy data from East Java Province during 2022. Based on data analysis, we obtain a model estimation with an R-square value of 96.24 %. At a 5 % significance level, the parameters simultaneously have a significant influence on the model. Partially, four parameters are not significant. However, overall, the predictor variables significantly influence the life expectancy data. • The Fourier series function used is a Fourier series function introduced by Bilodeau (1992). • The model estimation is obtained by selecting the optimal number of oscillation parameters. • The statistical test is obtained using the LRT method.

KW - Fourier series

KW - Life expectancy data

KW - Likelihood ratio test

KW - Nonparametric regression

KW - Statistical inference

UR - http://www.scopus.com/inward/record.url?scp=85201774005&partnerID=8YFLogxK

U2 - 10.1016/j.mex.2024.102922

DO - 10.1016/j.mex.2024.102922

M3 - Article

AN - SCOPUS:85201774005

SN - 2215-0161

VL - 13

JO - MethodsX

JF - MethodsX

M1 - 102922

ER -