Open Access

ARTICLE

The Robust Regression Methods for Estimating of Finite Population Mean Based on SRSWOR in Case of Outliers

Mir Subzar¹, Amer Ibrahim Al-Omari², Ayed R. A. Alanzi^{3, *}

1 Division of Agricultural Statistics SKUAST-K, Shalimar, 190025, India.
2 Department of Mathematics, Faculty of Science, Al al-Bayt University, Mafraq, 25113, Jordan.
3 Department of Mathematics, College of Science and Human Studies at HotatSudair, Majmaah University, Majmaah, 11952, Saudi Arabia.

* Corresponding Author: Ayed R. A. Alanzi. Email: .

Computers, Materials & Continua 2020, 65(1), 125-138. https://doi.org/10.32604/cmc.2020.010230

Received 18 February 2020; Accepted 03 June 2020; Issue published 23 July 2020

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.

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Citations