Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/34
Title: A new partial robust adaptive modified maximum likelihood estimator
Authors: Acıtaş, Şükrü
Filzmoser, Peter
Şenoğlu, Birdal
Keywords: Partial least squares
Robust adaptive modified likelihood
Outliers
Robust estimation
Issue Date: 2020
Publisher: Elsevier
Abstract: Partial least squares (PLS) regression is a widely-used regression method for high-dimensional data. However, PLS is not robust to outlying observations since it uses partial information of the variables in a least squares (LS) setting, which is known to be very sensitive to outliers. Several proposals are available which robustify PLS. In this study, our aim is to propose a new partial robust estimator using robust adaptive modified maximum likelihood (RAMML) estimators [1, 2]. The resulting estimators are therefore called partial robust adaptive modified maximum likelihood estimators (PRAMMLs). The distinguished advantage of the PRAMMLs is that they are computationally straightforward. This is because of the fact that they are constructed based on explicitly formulated estimators. The simulation study shows that the PRAMMLs are preferable to PLS and other existing robust alternatives of PLS in terms of the mean squared error (MSE) criterion under different nonnormal error distributions, as well as in the presence of leverage points. The PRAMMLs also give satisfactory results in terms of the empirical influence function and breakdown robustness criteria.
URI: https://doi.org/10.1016/j.chemolab.2020.104068
https://hdl.handle.net/20.500.13087/34
ISSN: 0169-7439
1873-3239
Appears in Collections:Fizik Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

SCOPUSTM   
Citations

3
checked on Feb 4, 2023

WEB OF SCIENCETM
Citations

6
checked on Feb 4, 2023

Page view(s)

16
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


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