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Item
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An Application of Ordinary Least Squares and Maximum Likelihood Type Estimation in Roust Diagnostic Regression Analysis (Maw Maw Khin, 2011)
http://hdl.handle.net/20.500.12678/0000001383
http://hdl.handle.net/20.500.12678/00000013835a97df94-82ea-47ef-8a9c-a6d26a9acfe7
43622850-f366-4aea-945d-ddb7b40bc76d
Name / File | License | Actions |
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Publication type | ||||||
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Journal article | ||||||
Upload type | ||||||
Other | ||||||
Title | ||||||
Title | An Application of Ordinary Least Squares and Maximum Likelihood Type Estimation in Roust Diagnostic Regression Analysis (Maw Maw Khin, 2011) | |||||
Language | en | |||||
Publication date | 2011-01-01 | |||||
Authors | ||||||
MAW MAW KHIN | ||||||
Description | ||||||
This study shows that the OLS method is quite sensitive to outlier whereas maximum likelihood type estimation {M-estimation) methods resist outliers. The iteratived reweighted least squares (IRLS) method based on the Huber and the Bisquare 'I' -functions clearly detect outliers that are given to less weight. The findings show that maximum likelihood type estimation based on the mean squares error (MSE) criterion can provide predicted values very close to actual values. |
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Keywords | ||||||
Robust Regression | ||||||
Journal articles | ||||||
Yangon University of Economics Research Journal | ||||||
Thesis/dissertations | ||||||
Yangon Institute of Economics |