A Simulation Study on Robust Alternatives of Least Squares Regression (Maw Maw Khin, 2019)

Maw Maw Khin, Dr.

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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

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Publication type
		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.
Keywords
		Robust Estimators
Journal articles
		1
		Yangon University of Economics
		6

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