A comparison among Different Methods for Estimating Regression Parameters with Autocorrelation Problem under Exponentially Distributed Error

Authors

Ahmed Dheyab Ahmed, Computer Center, College of Administrations and Economics, University of BaghdadFollow
Baydaa Ismael Abdulwahhab, Computer Center, College of Administrations and Economics, University of BaghdadFollow
Ebtisam Karim Abdulah, Computer Center, College of Administrations and Economics, University of BaghdadFollow

Abstract

Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of cigarettes according to the US Federal Trade Commission.

Keywords

Autocorrelation, Exponential distribution, General least squares method, Laplace robust method, M robust method, Multiple linear regression

Article Type

Supplemental Issue

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite this Article

Ahmed, Ahmed Dheyab; Abdulwahhab, Baydaa Ismael; and Abdulah, Ebtisam Karim (2020) "A comparison among Different Methods for Estimating Regression Parameters with Autocorrelation Problem under Exponentially Distributed Error," Baghdad Science Journal: Vol. 17: Iss. 3, Article 30.
DOI: https://doi.org/10.21123/bsj.2020.17.3(Suppl.).0980