A Penalized Least Squares Estimation of Fourier Series Semiparametric Regression Theory, Simulation, and Application
DOI:
https://doi.org/10.19139/soic-2310-5070-3139Keywords:
Estimation, Fourier Series Semiparametric Regression Model, Penalized Least Square, Generalized Cross Validation, Mean Average Percentage ErrorAbstract
In regression analysis, a functional relationship between response and predictor variables sometimes follows a semiparametric regression model that is constructed by parametric and nonparametric components where its nonparametric component is a function of time which will be approximated by a Fourier Series. In this study, we develop a penalized least square smoothing technique for estimating a Fourier Series Semiparametric Regression (FSSR) model. The penalized least square is very good to use when generalized cross validation method cannot choose really good parameters due to over-fitting effect in the model is ignored. We also provide numerical example through a simulation study, and apply the proposed method to real data for predicting temperature of earth surface based on relative humidity. The results show that the estimated FSSR model results MAPE value of 1.068% . This means that the obtained model has a highly accuracy category as a prediction modelDownloads
Published
2026-02-20
How to Cite
Amri, . I. F., Chamidah, N., Saifudin, T., Lestari, B., Aydin, D., & Rohim, F. (2026). A Penalized Least Squares Estimation of Fourier Series Semiparametric Regression Theory, Simulation, and Application. Statistics, Optimization & Information Computing, 15(4), 3255–3281. https://doi.org/10.19139/soic-2310-5070-3139
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Research Articles
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