E-Jurnal Matematika
Vol 5 No 2 (2016)

ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA

KOMANG CANDRA IVAN (Faculty of Mathematics and Natural Sciences, Udayana University)
I WAYAN SUMARJAYA (Faculty of Mathematics and Natural Sciences, Udayana University)
MADE SUSILAWATI (Faculty of Mathematics and Natural Sciences, Udayana University)



Article Info

Publish Date
31 May 2016

Abstract

Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.

Copyrights © 2016






Journal Info

Abbrev

mtk

Publisher

Subject

Mathematics

Description

E-Jurnal Matematika merupakan salah satu jurnal elektronik yang ada di Universitas Udayana, sebagai media komunikasi antar peminat di bidang ilmu matematika dan terapannya, seperti statistika, matematika finansial, pengajaran matematika dan terapan matematika dibidang ilmu lainnya. Jurnal ini lahir ...