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An Application of Ordinary Least Squares and Maximum Likelihood Type Estimation in Roust Diagnostic Regression Analysis
http://hdl.handle.net/20.500.12678/0000001383
http://hdl.handle.net/20.500.12678/00000013835a97df94-82ea-47ef-8a9c-a6d26a9acfe7
837f7698-06c7-4396-a4dd-3f1d874c9d92
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Journal article | ||||||
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Publication | ||||||
Title | ||||||
Title | An Application of Ordinary Least Squares and Maximum Likelihood Type Estimation in Roust Diagnostic Regression Analysis | |||||
Language | en | |||||
Publication date | 2011 | |||||
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 | ||||||
Identifier | https://oar-yueco.archive.knowledgearc.net/handle/123456789/40 | |||||
Journal articles | ||||||
Yangon University of Economics Research Journal | ||||||
Conference papaers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |