PERBANDINGAN METODE CROSS VALIDATION DAN GENERALIZED CROSS VALIDATION DALAM REGRESI NONPARAMETRIK BIRESPON SPLINE - Luh Putu Safitri Pratiwi (Jurnal Varian, 2017)
Regression analysis is one of the most popular methods in statistics to explain causal relationships between one predictor variables to one response variable. In general, modeling can be done using regression analysis. The regression curve can be assumed by the parametric regression approach and the nonparametric regression approach. However, not all data acquired follows a certain pattern so that this type of data uses a nonparametric regression approach. The nonparametric regression approach is not related to the assumption of the regression curve form as it is to the parametric regression, and more flexible. There are several techniques performed for estimation in nonparametric regression ie Spline. Some cases in the regression analysis found many problems that can not be solved by simple regression analysis of one response because if using two response variables in the research, it must be seen the value of correlation between variables. As a result, regression issues must be solved by the birespon regression model. This study aims to describe the IMR and malnutrition status of children under five and to get the Spline model in the best birespon nonparametric regression through the relationship between the suspected variables by using Cross Validation (CV) and Generalized Cross Validation (GCV) methods. The results obtained are the best model that is suitable for health by using CV method, obtained the minimum CV value located on Spline model linear one knot that is equal to 77.37831 with MSE of 76.75449.