ALTERNATIVE ROBUST ESTIMATORS FOR PARAMETERS OF THE LINEAR REGRESSION MODEL
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Tarih
2022
Yazarlar
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper considers parameter estimation of the linear regression model with Ramsay-Novick (RN) distributed errors, focusing on its use to aid robustness. Positioning within the class of heavy-tailed distributions, RN distribution can be defined as the modification of unbounded influence function of a non-robust density so that it has more resistance to outliers. \rPotential use of this robust density has been assessed in Bayesian settings on real data examples and there is a lack of performance assessment for finite samples in the classical approach. Therefore, this study explores its robustness properties when used as error distribution compared to normal and other alternating heavy-tailed distributions like Laplace and Studentt. An extensive simulation study was conducted for this purpose under different settings of sample size, model parameters and outlier percentages. An efficient data generation of RN distribution through random-walk Metropolis algorithm is here also suggested. The results were supported by a real world application on famously known as Brownlee’s stack loss plant data.
Açıklama
Anahtar Kelimeler
Matematik, Bilgisayar Bilimleri, Teori ve Metotlar, İstatistik ve Olasılık
Kaynak
Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler
WoS Q Değeri
Scopus Q Değeri
Cilt
10
Sayı
1
