International Journal For Multidisciplinary Research

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Eliminate the Heterogeneous Variances Effect Using Quantile Regression

Author(s) Abdelbaset Abdalla, Wafa Omar El_khafifi, Ahmed M. Mami, Nasir Elmesmari
Country Libyan Arab Jamahiriya
Abstract Quantile regression (QR) is a statistical method that addresses the issue of inconsistent data errors. QR utilizes minimum absolute deviation to reduce the absolute deviation by employing percentile estimators such as the median, 1st and 3rd quartiles, and 10th and 90th percentiles. This study focuses on the qth percentile estimators of QR. QR models not only detect varying effects of explanatory variables at different quantiles of the response variable but also provide more robust and accurate estimates compared to mean regression when normality assumptions are violated or when outliers and long tails are present. This study conducts several simulation studies to compare the suggested qth percentile estimators of QR under different sample sizes, explanatory variables, and qth percentiles. Additionally, the study examines the impact of error terms dependency on normal/non-normal distribution. The asymptotic properties of these estimators are also investigated. The study concludes with a discussion of the advantages and disadvantages of using these qth percentile estimators of QR.
Keywords Heterogeneous Variances Effect; Variance Inflation Factor (VIF); Quantile Regression
Field Mathematics > Statistics
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-27
Cite This Eliminate the Heterogeneous Variances Effect Using Quantile Regression - Abdelbaset Abdalla, Wafa Omar El_khafifi, Ahmed M. Mami, Nasir Elmesmari - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.8013
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.8013
Short DOI https://doi.org/gszvfj
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