Estimation Curve Semiparametric Regression with the Spline Linear Approach to Poverty Data in Bali Province

Abstract

In semiparametric regression, nonparametric components can be approached by spline. Splines are pieces of polynomial that are segmented and continuous. The one advantages of spline is the presence of knot points that indicate changes in the pattern of data behavior. This research purposetoobtain semiparametric regression curve estimation with linear spline approach. The method of optimization approach used by ordinary least square (OLS). Based on this research, there are two variables that have a significant effect on the percentage of poor people in Bali Province, namely the Open Unemployment the rate of economic growth. The total variance of response that can be explained by predictor in this model is 67.97% with MSE of 9.7854.