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KOMANG CANDRA IVAN
Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics

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ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA KOMANG CANDRA IVAN; I WAYAN SUMARJAYA; MADE SUSILAWATI
E-Jurnal Matematika Vol 5 No 2 (2016)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2016.v05.i02.p121

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.
Co-Authors I Wayan Sumarjaya Made Susilawati