PERBANDINGAN REGRESI ROBUST PENDUGA MM DENGAN METODE RANDOM SAMPLE CONSENSUS DALAM MENANGANI PENCILAN | Zendy

NI PUTU NIA IRFAGUTAMI | Zendy; I GUSTI AYU MADE SRINADI | Zendy; I WAYAN SUMARJAYA | Zendy

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PERBANDINGAN REGRESI ROBUST PENDUGA MM DENGAN METODE RANDOM SAMPLE CONSENSUS DALAM MENANGANI PENCILAN

Author(s) -

NI PUTU NIA IRFAGUTAMI,

I GUSTI AYU MADE SRINADI,

I WAYAN SUMARJAYA

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.

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