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

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Ahmed Dheyab Ahmed
https://orcid.org/0000-0002-1689-1376
Baydaa Ismael Abdulwahhab
https://orcid.org/0000-0002-0253-2460
Ebtisam Karim Abdulah
https://orcid.org/0000-0002-3326-0576

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.

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How to Cite
1.
Ahmed A, Abdulwahhab B, Abdulah E. A comparison among Different Methods for Estimating Regression Parameters with Autocorrelation Problem under Exponentially Distributed Error. BSJ [Internet]. 8Sep.2020 [cited 26Sep.2020];17(3(Suppl.):0980. Available from: http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/3425
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