Nisa, Khoirin (2007) PEMBANDINGAN EMPIRIS TIGA METODE REGRESI ROBUST (AN EMPIRICAL COMPARISON OF THREE ROBUST REGRESSION METHODS). PROSIDING Seminar Hasil Penelitian & Pengabdian Kepada Masyarakat UNIVERSITAS LAMPUNG BANDARLAMPUNG SEPTEMBER Universitas Lampung. ISSN ISBN 978-979-15535-1-3

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Abstract

Regression analysis is a statistical technique that serves as a basis for drawing inferences about relationship among variables (Myers, 1990). When data contains outlier, a robust technique on regression is urgently needed. In this paper we aim to compare three robust regressions methods: Least Trimmed Square (LTS), Least Median Square (LMS) and Least Absolute Value (LAV). We set a Monte Carlo simulation using 1000 random samples on every sample size we considered: n = 30, 60,100, and 200. We contaminated the data with 10%, 20%, 30% and 40% outliers. The effect of outliers on regression coefficient is studied by comparing the bias, the mean square error (MSE), and the standard error (SE) resulted by LTS, LMS and LAV in presence of outliers. The result shows that the LMS and LTS yield almost the same bias, MSE and SE. And each of the two methods performs better than LAV.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Depositing User: DR. KHOIRIN NISA
Date Deposited: 27 Mar 2018 04:44
Last Modified: 27 Mar 2018 04:44
URI: http://repository.lppm.unila.ac.id/id/eprint/6617

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