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Item
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A Simulation Study on Robust Alternatives of Least Squares Regression (Maw Maw Khin, 2019)
http://hdl.handle.net/20.500.12678/0000001610
http://hdl.handle.net/20.500.12678/000000161038ce81dc-79e3-470b-a28d-18d6ad2857c5
2404a140-4f1b-4bcf-8fcd-26305fb8ee38
Name / File | License | Actions |
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Publication type | ||||||
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Journal article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | A Simulation Study on Robust Alternatives of Least Squares Regression (Maw Maw Khin, 2019) | |||||
Language | en | |||||
Publication date | 2019-11-01 | |||||
Authors | ||||||
Maw Maw Khin, Dr. | ||||||
Description | ||||||
Five methods of regression namely the ordinary least squares, least absolute value, M, least median squares and least trimmed squares are applied to the multiple regression model. The several distributional assumptions of errors are considered in this study. The required data sets are generated by using multiple linear regression models with three explanatory variables. Then, these data sets are transformed into outlier contaminated data sets. After that, the performances are compared in terms of bias and mean squared errors criteria and then the most suitable estimation method is chosen. Same sets of simulated data are used and mean squared errors and bias of these methods are compared. It is found that ordinary least squares estimation under a heavy-tailed distribution does not yield outlier robust estimates. Indeed, not only with the Gaussian distribution but also with the skewed distributions, ordinary least squares estimators collapse in the presence of small levels of outlier contamination. The Huber M-estimate and bisquare M-estimate estimate have shown to be more appropriate alternatives to the ordinary least squares in heavy-tailed distributions whereas the LMS estimates are better choices for skewed data. One best method could not be suggested in all situations; however the use of more than one method of exploratory data analysis is recommended in practice. |
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Keywords | ||||||
Robust Estimators | ||||||
Journal articles | ||||||
1 | ||||||
Yangon University of Economics | ||||||
6 |