Aplikasi Spline Truncated dalam Regresi Nonparametrik

Abstract

Spline truncated regression is one of the nonparametric approach model that has been modified from segmented polynomial .The estimator form of spline is being strongly influenced by λ as the value of smoothing parameter which is essentially determining the location of knots. This research aims to examines the usage of the least squares method with a matrix approach in order to determines estimator the spline linear regression two knots well as to recognize the best method as the criteria for the optimal knots, specifically MSE and GCV. As the result of this research, it shows if the regression estimator can be solved with the least squares method through a matrix approach. The usage of the least squares method assume the form of spline functions and provide the easier interpretation way through statistical models. Meanwhile from the data output voltage cencorship polymer, it discovered that the selection of the best spline regression model using MSE (λ) is equal to 0.760617 and GCV (λ) is equal to 1.188464. The minimum result of both methods constantly showed the same knot point location in every trial error. It is indicated if the result shows that both methods have the same effectiveness in determining the optimal location of the point knots. However, refering to the value result, the value of MSE (λ) is the minimum value and it could be recognized as the best method since it is quite efficient and easier to be used in spline linear regression.