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.
Highlights
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
able to deal with outliers are
Summary
Analisis regresi merupakan metode statistika yang bertujuan untuk menganalisis hubungan atau pengaruh antara satu atau lebih peubah prediktor terhadap peubah respons. Metode yang mampu menghasilkan penduga parameter yang robust atau kekar terhadap adanya pencilan pada analisis regresi linear berganda adalah Minimum Covariance Determinant (MCD) dan Least Absolute. Metode estimasi yang digunakan untuk menduga parameter serta memiliki sifat tidak bias adalah Metode Kuadrat Terkecil (MKT) atau Ordinary Least of Square (OLS). Perlu dilakukannya penelitian terhadap pengaplikasian bootstrap atau resampling pada metode MCD dan metode LAD dengan data yang berjumlah sedikit. Berdasarkan pemaparan di atas, penulis tertarik melakukan perbandingan terhadap metode MCD Bootstrap dan LAD Bootstrap dalam mengatasi pengaruh pencilan pada analisis regresi linear berganda. Hal ini dapat ditunjukkan dengan membandingkan bias penduga parameter dan lebar selang yang dihasilkan oleh metode MCD Bootstrap dan LAD Bootstrap.
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