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Item

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  1. Yangon University of Economics
  2. Yangon University of Economics Research Journal and Universities Research Journal

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/20.500.12678/0000001610
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2404a140-4f1b-4bcf-8fcd-26305fb8ee38
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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.
Keywords
Robust Estimators
Journal articles
1
Yangon University of Economics
6
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