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OUTLIERS AND THEIR EFFECT ON PARAMETERS ESTIMATIONS IN REGRESSION ANALYSIS (Maw Maw Khin, 2018)

http://hdl.handle.net/20.500.12678/0000006825
8c9682d2-da1a-41d6-baad-5190428a9d60
d940ce45-2ca0-4829-b619-8938da460972
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Dr. Dr. Maw Maw Khin.pdf (423 KB)
Publication type
Journal article
Upload type
Publication
Title
Title OUTLIERS AND THEIR EFFECT ON PARAMETERS ESTIMATIONS IN REGRESSION ANALYSIS (Maw Maw Khin, 2018)
Language en
Publication date 2018-02-01
Authors
Maw Maw Khin
Description
This study attempts to investigate the effect of outliers on estimation of parameters in regression analysis.The results about outlier robustness point out that the robust and classical methods both worked well data with no outliers indicating that their mean squares error (MSE) are quite close to each other. If there are outliers in the data, the robust methods perform better than the classical method. The OLS estimates provide poor estimates of true parameters of the regression model. As expected, OLS is a less efficient estimator whatever the type of outliers present in the data.
Keywords
RobustEstimators, Maximum Likelihood, Additive Outlier
Journal articles
Yangon University of Economics Research Journal
81-89
vol.5, no.1
0
0
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