THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH

Abstract

Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression. In this study, the form of spline semiparametric regression curve estimator for longitudinal data assessed. Based on the estimator that be obtained by using Weighted Least Square (WLS) optimization applied to model electricity consumption in Madura by choosing a model for longitudinal data based on linear spline estimator with two knot. The good criterion of the model is using the GCV value, the coefficient of determination and the value of MSE. The best model is a model that has a high coefficient of determination and a small MSE value. This spline model has a determination coefficient value of 99,72911% and MSE 32,50458.