Outlier detection in gamma regression using Pearson residuals: Simulation and an application

AIMS Mathematics

article

Muhammad Amin¹  Saima Afzal²  Muhammad Nauman Akram¹  Abdisalam Hassan Muse³  Ahlam H. Tolba⁴  Tahani A. Abushal⁵ 

[1] Department of Statistics, University of Sargodha;Department of Statistics, Bahauddin Zakariya University;Department of Mathematics ,(Statistics Option), Pan African University, Institute for Basic Sciences;Mathematics Department, Faculty of Science, Mansoura University;Department of Mathematical Science, Faculty of Applied Science, Umm AL-Qura University

关键词: adjusted Pearson residuals;    gamma regression;    outlier detection;    Pearson residuals;   

DOI  :  10.3934/math.2022840

学科分类：地球科学(综合)

来源: AIMS Press

【 摘 要 】

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.

【 授权许可】

CC BY