<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10001181" mdate="2015-04-02 00:00:00"> <author>F. Z. Do臒ru and O. Arslan</author> <title>Alternative Robust Estimators for the Shape Parameters of the Burr XII Distribution</title> <pages>271 - 276</pages> <year>2015</year> <volume>9</volume> <number>5</number> <journal>International Journal of Mathematical and Computational Sciences</journal> <ee>https://publications.waset.org/pdf/10001181</ee> <url>https://publications.waset.org/vol/101</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In general, classical methods such as maximum likelihood (ML) and least squares (LS) estimation methods are used to estimate the shape parameters of the Burr XII distribution. However, these estimators are very sensitive to the outliers. To overcome this problem we propose alternative robust estimators based on the Mestimation method for the shape parameters of the Burr XII distribution. We provide a small simulation study and a real data example to illustrate the performance of the proposed estimators over the ML and the LS estimators. The simulation results show that the proposed robust estimators generally outperform the classical estimators in terms of bias and root mean square errors when there are outliers in data. </abstract> <index>Open Science Index 101, 2015</index> </article>