Robust multiple regression based on shrinkage $\mathbf{S}_{n}$ estimator

Lakshmi R; Sajesh T A

Makale detayları

Robust multiple regression based on shrinkage $\mathbf{S}_{n}$ estimator

Yazarlar:

Lakshmi R | Sajesh T A

Yayıncı:

Hacettepe University

Ana Başlıklar:

Computational Statistics | Statistical Analysis | Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1062-1093

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351218

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 Regression analysis is used to model the data statistically. However, data modeling and interpretation are affected by outliers and significant points. Robust regression analysis offers an alternative. In this study, the parameters that define the linear regression problem are estimated using a robust approach. The concept of shrinkage, which has been investigated for outlier detection in multivariate data. A comprehensive simulation analysis is performed to examine the breakdown value of the regression estimator, the affine equivariance, the robustness against contamination, and the efficiency with normal errors. The advantages of the suggested robust estimator in regression are demonstrated by the simulation results and real-world data examples. Simulation and research are conducted using the R software.

 Regression analysis is used to model the data statistically. However, data modeling and interpretation are affected by outliers and significant points. Robust regression analysis offers an alternative. In this study, the parameters that define the linear regression problem are estimated using a robust approach. The concept of shrinkage, which has been investigated for outlier detection in multivariate data. A comprehensive simulation analysis is performed to examine the breakdown value of the regression estimator, the affine equivariance, the robustness against contamination, and the efficiency with normal errors. The advantages of the suggested robust estimator in regression are demonstrated by the simulation results and real-world data examples. Simulation and research are conducted using the R software.

 Regression analysis is used to model the data statistically. However, data modeling and interpretation are affected by outliers and significant points. Robust regression analysis offers an alternative. In this study, the parameters that define the linear regression problem are estimated using a robust approach. The concept of shrinkage, which has been investigated for outlier detection in multivariate data. A comprehensive simulation analysis is performed to examine the breakdown value of the regression estimator, the affine equivariance, the robustness against contamination, and the efficiency with normal errors. The advantages of the suggested robust estimator in regression are demonstrated by the simulation results and real-world data examples. Simulation and research are conducted using the R software.

 Regression analysis is used to model the data statistically. However, data modeling and interpretation are affected by outliers and significant points. Robust regression analysis offers an alternative. In this study, the parameters that define the linear regression problem are estimated using a robust approach. The concept of shrinkage, which has been investigated for outlier detection in multivariate data. A comprehensive simulation analysis is performed to examine the breakdown value of the regression estimator, the affine equivariance, the robustness against contamination, and the efficiency with normal errors. The advantages of the suggested robust estimator in regression are demonstrated by the simulation results and real-world data examples. Simulation and research are conducted using the R software.

Makale detayları

Robust multiple regression based on shrinkage $\mathbf{S}_{n}$ estimator

Yazarlar:

Lakshmi R | Sajesh T A

Yayıncı:

Hacettepe University

Ana Başlıklar:

Computational Statistics | Statistical Analysis | Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1062-1093

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351218

pISSN:

2651-477X

Data modeling

regression

reweighted estimator

robust shrinkage $S_{n}$

Sayı

Makale detayları

Robust multiple regression based on shrinkage $\mathbf{S}_{n}$ estimator

Yazarlar: Lakshmi R | Sajesh T A

Yayıncı: Hacettepe University

Ana Başlıklar: Computational Statistics | Statistical Analysis | Applied Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1062-1093

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351218

pISSN: 2651-477X

Data modeling

regression

reweighted estimator

robust shrinkage $S_{n}$

Sayı

Yazarlar:

Lakshmi R | Sajesh T A

Yayıncı:

Hacettepe University

Ana Başlıklar:

Computational Statistics | Statistical Analysis | Applied Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.54 Issue 3, pp.1062-1093

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351218

pISSN:

2651-477X