Simulation study for biresponses nonparametric regression model using MARS

In statistical modeling, especially regression analysis, we can find relationship pattern between two responses with several predictors and both of responses are correlated each other. When the assumption of the pattern is unknown, then the regression parameters could be obtained by using biresponses nonparametric regression. One method that often used in nonparametric regression with single response is Multivariate Adaptive Regression Spline (MARS). This paper aims to know how ability of MARS in estimating biresponses nonparametric regression through simulation study on different sample size (n) and variance error (σ²). We use R-square and MSE as the goodness of fit criterion. Result shows that the smaller variance error gives better estimation than the bigger one, because it gives higher R-square and smaller MSE values. Whereas the variation of sample size gives small effect on the accuracy of the model, because the value of R-square and MSE in this case tend to be the same on different sample sizes.