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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
5a97df94-82ea-47ef-8a9c-a6d26a9acfe7
43622850-f366-4aea-945d-ddb7b40bc76d
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Dr.Maw Dr.Maw Maw Khin.pdf (3916 Kb)
Publication type
Journal article
Upload type
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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.
Keywords
Robust Regression
Journal articles
Yangon University of Economics Research Journal
Thesis/dissertations
Yangon Institute of Economics
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