PERBANDINGAN METODE MCD-BOOTSTRAP DAN LAD-BOOTSTRAP DALAM MENGATASI PENGARUH PENCILAN PADA ANALISIS REGRESI LINEAR BERGANDA
Abstract: 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.
Penulis: NI LUH PUTU RATNA
KUMALASARI, NI LUH PUTU SUCIPTAWATI, MADE SUSILAWATI
Kode Jurnal: jpmatematikadd170170
