@article{Amin2022, 
author = {Muhammad Amin and Saima Afzal and Muhammad Nauman Akram and Abdisalam Hassan Muse and Ahlam H. Tolba and Tahani A. Abushal},
title = {Outlier detection in gamma regression using Pearson residuals: Simulation and an application},
year = {2022},
journal = {AIMS Mathematics},
volume = {7},
number = {8},
pages = {15331-15347},
keywords = {adjusted Pearson residuals, gamma regression, outlier detection, Pearson residuals},
url = {https://www.sciopen.com/article/10.3934/math.2022840},
doi = {10.3934/math.2022840},
abstract = {In data analysis, the choice of an appropriate regression model and outlier detection are both very important in obtaining reliable results. Gamma regression (GR) is employed when the distribution of the dependent variable is gamma. In this work, we derived new methods for outlier detection in GR. The proposed methods are based upon the adjusted and standardized Pearson residuals. Furthermore, a comparison of available and proposed methods is made using a simulation study and a real-life data set. The results of simulation and real-life application the evidence better performance of the adjusted Pearson residual based outlier detection approach.}
}