Confidence interval for mixed parameters of spline truncated and fourier series in nonparametric regression models

Regression is defined as a statistical method that can be used to determine the relationship between response and predictor variables. If the data does not form a certain pattern, the approach that can be used is nonparametric regression. Several approaches can be used in nonparametric path analysis are Truncated Spline and Fourier Series. When the data does not form a particular relationship pattern but tends to repeat itself, the approach used is the Fourier Series approach. One crucial aspect of inferential statistics is determining the confidence interval. The diversity of data with numerous factors has possibilities where these factors may show repeating patterns or none at all, hence allowing for approximation using a mixed Truncated Spline and Fourier Series method. The development of a mixed Truncated Spline and Fourier Series method in confidence interval has not been extensively explored. Therefore, this research aims to determine parameter confidence intervals using a mixed Truncated Spline and Fourier Series method. The results of this research is the shortest confidence intervals for mixed parameters of spline truncated and Fourier series in nonparametric regression model.