@Article{cmc.2020.010230,
AUTHOR = {Mir Subzar, Amer Ibrahim Al-Omari, Ayed R. A. Alanzi},
TITLE = {The Robust Regression Methods for Estimating of Finite  Population Mean Based on SRSWOR in Case of Outliers},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {125--138},
URL = {http://www.techscience.com/cmc/v65n1/39557},
ISSN = {1546-2226},
ABSTRACT = {The ordinary least square (OLS) method is commonly used in regression 
analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the 
robust regression methods have been suggested for a long time as alternatives to the OLS 
to solve the outliers problem. In the present study, new ratio type estimators of finite 
population mean are suggested using simple random sampling without replacement 
(SRSWOR) utilizing the supplementary information in Bowley’s coefficient of skewness 
with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows 
GM-estimate, Schweppe GM-estimate, and SIS GM-estimate methods for estimating the 
population parameters. Theoretically, the mean square error (MSE) equations of various 
estimators are obtained and compared with the OLS competitor. Simulations for skewed 
distributions as the Gamma distribution support the results, and an application of real data 
set containing outliers is considered for illustration.},
DOI = {10.32604/cmc.2020.010230}
}