The sinusoidal model has many applications in time series analysis, signal processing, regression, and other phenomena that are repeated periodically. On the other hand, smoothing spline is a flexible and useful method in many fields. In this article, smoothing spline is applied to interpolate data generated from the sinusoidal model. Therefore, a sinusoidal model is considered in three general forms. Then, in a simulation study, data sets are generated from each of the sinusoidal model forms, and the effect of changing the model components is assessed. Besides, the smoothing spline method is applied to estimate the related sinusoidal model, and the performance of the smoothing spline for fitting a proper model to the sinusoidal data is studied. Furthermore, by fitting a proper sinusoidal model to each generated data set, the performance of smoothing spline is compared with the sinusoidal model. The sum of squares error criterion is applied to compare the performance of models. The simulation results illustrate that smoothing spline has better performance for model fitting to sinusoidal data.
Alimohammadi, R. (2022). Application of smoothing spline in sinusoidal modeling. Journal of Statistical Modelling: Theory and Applications, 3(2), 169-173. doi: 10.22034/jsmta.2023.20336.1106
MLA
Roshanak Alimohammadi. "Application of smoothing spline in sinusoidal modeling". Journal of Statistical Modelling: Theory and Applications, 3, 2, 2022, 169-173. doi: 10.22034/jsmta.2023.20336.1106
HARVARD
Alimohammadi, R. (2022). 'Application of smoothing spline in sinusoidal modeling', Journal of Statistical Modelling: Theory and Applications, 3(2), pp. 169-173. doi: 10.22034/jsmta.2023.20336.1106
VANCOUVER
Alimohammadi, R. Application of smoothing spline in sinusoidal modeling. Journal of Statistical Modelling: Theory and Applications, 2022; 3(2): 169-173. doi: 10.22034/jsmta.2023.20336.1106
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