PROSIDING SEMINAR NASIONAL
2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi

KAJIAN PEMODELAN SPLINE UNTUK DATA LONGITUDINAL SEBAGAI PERKEMBANGAN DARI REGRESI NONPARAMETRIK

Suparti Suparti (Unknown)
Alan Prahutama (Unknown)
Rukun Santoso (Unknown)



Article Info

Publish Date
18 Oct 2017

Abstract

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.

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