Bi-responses nonparametric regression model using MARS and its properties

Abstract

In many cases of regression analysis, we can find unknown relationship pattern between two response variables with many predictor variables (multi-predictor) and both of responses are correlated each other. Consequently such regression problem should be solved using bi-responses nonparametric regression model, which able to overcome high dimensionality case in multi-predictor data. One of the methods is Multivariate Adaptive Regression Spline (MARS). This paper aims to study how MARS method could be utilized on bi-responses nonparametric regression model. The study begins with obtaining model equation form. Thus we estimating regression function and weighted from its model equation. Last step is. The results also show that the properties of this model is a bias estimator and linear in observation y ɶ .

Determination of the best multivariate adaptive geographically weighted generalized Poisson regression splines model employing generalized cross-validation in dengue fever cases

“…Some studies that demonstrate the advantages of MARS compared to other regression models incorporate [2] , [3] , [4] , [5] . Most MARS are developed for continuous [6] , [7] , [8] , [9] and categorical responses [10] , [11] , [12] , [13] , however, MARS for count responses are still scarce and limited. MARS with count response is the result of combining MARS and Poisson regression, first discussed by Yasmirullah et al.…”