Experimental Approach for the Evaluation of the Performance of a Satellite Module in the CanSat Form Factor for In Situ Monitoring and Remote Sensing Applications

Table 8

Nonparametric regression method comparison for model selection between and variables.

Method	Model equation	Validation	Grade	Df	MSE	RMSE		References
Polynomial regression		Leave one out cross-validation (LOOCV); one-way ANOVA	3		699.7394	26.45259	-0.8725435	[49, 50]
Step functions		-fold cross-validation	—	—	—	—	—	[49, 51, 52]
Regression splines		-fold cross-validation	2,3	4-16	698.1634	26.42278	-0.8583486	[49, 51, 52]
Smoothing splines		-fold cross-validation	3	92.72798	698.1634	26.42278	-0.8583486	[49, 51]
Local regression	Local linear regression (LOESS); Local polynomial regression	Assign weights (kernel functions)			696.9988	26.40073	-0.8552487	[49, 53, 54]

The step functions were only used for the degrees of the functions of the regression splines. The function is named kernel function, and in general, it is a continuous density function, unimodal, and symmetric around 0. The parameter is known as the smoothing parameter.