Open AccessPERBANDINGAN REGRESI ROBUST PENDUGA MM DENGAN METODE RANDOM SAMPLE CONSENSUS DALAM MENANGANI PENCILAN
Author(s) -
Putu Nia Irfagutami,
I Gusti Ayu,
I Gusti Ayu Made Srinadi,
I Wayan Sumarjaya,
Bukit Jimbaran-Bali
Publication year - 2014
Publication title -
e-jurnal matematika
Language(s) - English
Resource type - Journals
ISSN - 2303-1751
DOI - 10.24843/mtk.2014.v03.i02.p065
Subject(s) - ransac , outlier , estimator , robust regression , robust statistics , mathematics , statistics , mean squared error , ordinary least squares , minimum variance unbiased estimator , robustness (evolution) , trimmed estimator , econometrics , consistent estimator , computer science , artificial intelligence , image (mathematics) , biochemistry , chemistry , gene
The presence of outliers in observation can result in biased in parameter estimation using ordinary least square (OLS). Robust regression MM-estimator is one of the estimations methods that able to obtain a robust estimator against outliers. Random sample consensus (ransac) is another method that can be used to construct a model for observations data and also estimating a robust estimator against outliers. Based on the study, ransac obtained model with less biased estimator than robust regression MM-estimator.