KAJIAN PEMODELAN SPLINE UNTUK DATA LONGITUDINAL SEBAGAI PERKEMBANGAN DARI REGRESI NONPARAMETRIK - Suparti Suparti; Alan Prahutama; Rukun Santoso (Universitas Muhammadiyah Semarang, 2017)
Regression analysis can be approached by using parametric, semi-parametricand nonparametric regression approaches. One of nonparametric regressionapproach that great developed was Spline truncated, including for modelinglongitudinal data. Longitudinal data is data that consisting of several subjectswhich is each subject is observed repeatedly based on a certain time. Theadvantages of longitudinal data has provided more complexcityof informationthan cross section and time series data. The spline approach was a segmentedpolynomial regression approach. Spline provides high flexibility due to the useof knot points. To determine the optimal knot points using Generalized CrossValidation (GCV). The principle of determining the optimum point of knot oflongitudinal data using spline truncated is basically the same as with Splinemethod for cross section data, that is determination of knot point based on eachsubject. However, the estimation is done simultaneously so that each subject hasits own model. Keywords: Spline Truncated, GCV, Knot points.