The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON MATHEMATICS
Transactions ID Number: 29-388
Full Name: Habshah Midi
Position: Associate Professor
Age: ON
Sex: Female
Address: Laboratory of Applied and Computational Statistics,Institute for Mathematical Research,University Putra Malaysia,43400 Serdang, Selangor, MALAYSIA
Country: MALAYSIA
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E-mail address: habshahmidi@gmail.com
Other E-mails: srana_stat@yahoo.com
Title of the Paper: The Performance of Robust Weighted Least Squares in the Presence of Outliers and Heteroscedastic Errors
Authors as they appear in the Paper: Habshah Midi, Sohel Rana, A.H.M.R. Imon
Email addresses of all the authors: habshahmidi@gmail.com,srana_stat@yahoo.com,imon_ru@yahoo.com
Number of paper pages: 11
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 ob¬servations 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 estimat!
ion 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 de¬termining 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.
Keywords: Heteroscedasticity, Outliers, Robust estimation, Robust weighted least squares, Monte carlo simulation
EXTENSION of the file: .doc
Special (Invited) Session: Robust Estimation of Regression Parameters with Heteroscedastic Errors in the Presence of Outliers
Organizer of the Session: 613-237
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