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Exploring the performance of the GPSC method under several levels of outliers

Pérez Garrido, Betsabé (2023) Exploring the performance of the GPSC method under several levels of outliers. HUNGARIAN STATISTICAL REVIEW, 6 (2). pp. 3-11. ISSN 2630-9130

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Abstract

The aim of this study is to evaluate the performance of the Groupwise Principal Sensitivity Components method which is a robust iterative procedure for fitting linear regression models with fixed group effects. A simulation study is carried out to assess its ability to detect multiple outliers located in the response variable (vertical outliers) or in the explanatory and response variable (high leverage outliers). Several levels of outliers are considered ranging from 5% to 45% within selected groups. The results suggest that the GPSC method is able to avoid the masking effect under low or moderate level of outliers -approximately below to 30%. Additionally, in almost all cases the GPSC method reports lower levels of false outlier detection under high leverage outliers.

Item Type: Article
Uncontrolled Keywords: linear regression model with fixed effects, outlier detection, robust method, swamping effect, masking effect
Subjects: H Social Sciences / társadalomtudományok > HA Statistics / statisztika
H Social Sciences / társadalomtudományok > HG Finance / pénzügy
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 03 Aug 2024 08:32
Last Modified: 03 Aug 2024 08:32
URI: https://real.mtak.hu/id/eprint/201666

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