Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/33
Title: A robust adaptive modified maximum likelihood estimator for the linear regression model
Authors: Acıtaş, Şükrü
Filzmoser, Peter
Şenoğlu, Birdal
Keywords: Adaptive modified likelihood
efficiency
leverage point
regression
robustness
Issue Date: 2021
Publisher: Taylor & Francis Ltd
Abstract: Robust estimators are widely used in regression analysis when the normality assumption is not satisfied. One example of robust estimators for regression is adaptive modified maximum likelihood (AMML) estimators [Donmez A. Adaptive estimation and hypothesis testing methods [dissertation]. Ankara: METU; 2010]. However, they are not robust to x outliers, so-called leverage points. In this study, we propose a new estimator called robust AMML (RAMML) which is not only robust to y outliers but also to x outliers. A simulation study is carried out to compare the performance of the RAMML estimators with some existing robust estimators. The results show that the RAMML estimators are preferable in most of the settings according to the mean squared error (MSE) criterion. Two data sets taken from the literature are also analyzed to show the implementation of the RAMML estimation methodology.
URI: https://doi.org/10.1080/00949655.2020.1856847
https://hdl.handle.net/20.500.13087/33
ISSN: 0094-9655
1563-5163
Appears in Collections:İstatistik Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

WEB OF SCIENCETM
Citations

1
checked on Jul 14, 2022

Page view(s)

28
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.