Spline and kernel mixed estimators in multivariable nonparametric regression for dengue hemorrhagic fever model

Abstract

This article discusses statistical innovations implemented in the health sector. The research is being conducted on the treatment and prevention of Dengue Hemorrhagic Fever (DHF), focusing on the factors contributing to the increase in DHF. Create a nonparametric regression model with a mixed estimator, truncated spline, and Gaussian Kernel to estimate the regression curve. In multiple nonparametric regression, this method can handle differences in data patterns between predictors. Truncated splines are polynomial segments with segmented and continuous properties. Truncated splines contain knot points that can locate their estimated data no matter where the data pattern moves. In addition, the Gaussian Kernel estimator is dependent on bandwidth, which regulates the regression curve's smoothness. The mixed estimators of truncated spline and Gaussian Kernel could model DHF cases according to an empirical analysis of DHF data. The most effective model has a Coefficient of Determination (R2) of 88.46%. Simultaneous hypothesis testing indicates that the model contains at least one significant predictor variable.