Open AccessPERBANDINGAN METODE MCD-BOOTSTRAP DAN LAD-BOOTSTRAP DALAM MENGATASI PENGARUH PENCILAN PADA ANALISIS REGRESI LINEAR BERGANDA
Author(s) -
Ni Luh Putu Ratna Kumalasari,
Ni Luh Putu Suciptawati,
Made Susilawati
Publication year - 2017
Publication title -
e-jurnal matematika
Language(s) - English
Resource type - Journals
ISSN - 2303-1751
DOI - 10.24843/mtk.2017.v06.i01.p147
Subject(s) - statistics , confidence interval , outlier , mathematics , least absolute deviations , resampling , estimator , robust confidence intervals , linear regression
Outliers are observations that are far away from other observations. Outlier can be interfered with the process of data analysis which influence the regression parameters estimation. Methods that are able to deal with outliers are Minimum Covariance Determinant and Least Absolute Deviation methods. However, if both methods are applied with small sample the validity of both methods is being questioned. This research applies bootstrap to MCD and LAD methods to small sample. Resampling using 500, 750,and 1000 with confidence interval of 95% and 99% shows that both methods produce an unbiased estimators at 10%, 15%, and 20% outliers. The confidence interval of MCD-Bootstrap method is shorter than LAD-Bootstrap method. Both are, MCD-Bootstrap method is a better thus than LAD-Bootstrap method.