Citation
Midi, Habshah and Rana, Md. Sohel and Imon, A. H. M. Rahmatullah
(2009)
The performance of robust weighted least squares in the presence of outliers and heteroscedastic errors.
WSEAS Transactions on Mathematics, 8 (7).
pp. 351-361.
ISSN 1109-2769
Abstract
The Ordinary Least Squares (OLS) method is the most popular technique in statistics and is often use
to estimate the parameters of a model because of tradition and ease of computation. The OLS provides an
efficient and unbiased estimates of the parameters when the underlying assumptions, especially the assumption
of contant error variances (homoscedasticity), are satisfied. Nonetheless, in real situation it is difficult to retain
the error variance homogeneous for many practical reasons and thus there arises the problem of
heteroscedasticity. We generally apply the Weighted Least Squares (WLS) procedure to estimate the regression
parameters when heteroscedasticity occurs in the data. Nevertheless, there is evidence that the WLS estimators
suffer a huge set back in the presence of a few atypical observations that we often call outliers. In this situation
the analysis will become more complicated. In this paper we have proposed a robust procedure for the
estimation of regression parameters in the situation where heteroscedasticity comes together with the existence
of outliers. Here we have employed robust techniques twice, once in estimating the group variances and again in
determining weights for the least squares. We call this method Robust Weighted Least Squares (RWLS). The
performance of the newly proposed method is investigated extensively by real data sets and Monte Carlo
Simulations. The results suggest that the RWLS method offers substantial improvements over the existing
methods.
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Additional Metadata
Item Type: |
Article
|
Subject: |
Least squares |
Subject: |
Error analysis (Mathematics) |
Subject: |
Robust statistics |
Divisions: |
Faculty of Science |
Publisher: |
World Scientific and Engineering Academy and Society |
Keywords: |
Heteroscedasticity; Outliers; Robust estimation; Robust Weighted Least Squares; Monte Carlo Simulation. |
Depositing User: |
Najwani Amir Sariffudin
|
Date Deposited: |
25 Jun 2012 00:59 |
Last Modified: |
22 Jul 2016 03:17 |
URI: |
http://psasir.upm.edu.my/id/eprint/17266 |
Statistic Details: |
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