Testing the Significance of Regression Coefficients in Liu Type Estimators

Hilal Kaplan Tabak; Meral Ebegil; Esra Gökpınar

Makale detayları

Testing the Significance of Regression Coefficients in Liu Type Estimators

Yazarlar:

Hilal Kaplan Tabak | Meral Ebegil | Esra Gökpınar

Yayıncı:

Gazi University

Ana Başlıklar:

Applied Statistics

Dergi:

Gazi University Journal of Science Vol.37 Issue 4, pp.2062-2083

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331647

pISSN:

2147-1762

Anahtar kelimeler:

Özet:

 In the linear regression model, the multicollinearity problem arises when there is a linear relationship between independent variables. This situation causes the variance of the estimations of the model parameters obtained by the Least Squares Estimator method to increase and move away from the true value, resulting in unstable and incorrect results. Biased Estimator methods are developed to eliminate the adverse effects caused by multicollinearity. In this study, a test statistic is obtained to test the significance of the model coefficients for the Liu-Type Estimator using the test statistic method suggested in the study of Halawa and El-Bassiouni 2000. With a simulation study, the significance of the model coefficients of the Ridge, Liu, and Liu type biased estimators in different situations is tested; the type I errors and power values of the estimators are calculated; the results are compared. In addition, a real data application is performed to better understand the test procedure.

 In the linear regression model, the multicollinearity problem arises when there is a linear relationship between independent variables. This situation causes the variance of the estimations of the model parameters obtained by the Least Squares Estimator method to increase and move away from the true value, resulting in unstable and incorrect results. Biased Estimator methods are developed to eliminate the adverse effects caused by multicollinearity. In this study, a test statistic is obtained to test the significance of the model coefficients for the Liu-Type Estimator using the test statistic method suggested in the study of Halawa and El-Bassiouni 2000. With a simulation study, the significance of the model coefficients of the Ridge, Liu, and Liu type biased estimators in different situations is tested; the type I errors and power values of the estimators are calculated; the results are compared. In addition, a real data application is performed to better understand the test procedure.

 In the linear regression model, the multicollinearity problem arises when there is a linear relationship between independent variables. This situation causes the variance of the estimations of the model parameters obtained by the Least Squares Estimator method to increase and move away from the true value, resulting in unstable and incorrect results. Biased Estimator methods are developed to eliminate the adverse effects caused by multicollinearity. In this study, a test statistic is obtained to test the significance of the model coefficients for the Liu-Type Estimator using the test statistic method suggested in the study of Halawa and El-Bassiouni 2000. With a simulation study, the significance of the model coefficients of the Ridge, Liu, and Liu type biased estimators in different situations is tested; the type I errors and power values of the estimators are calculated; the results are compared. In addition, a real data application is performed to better understand the test procedure.

 In the linear regression model, the multicollinearity problem arises when there is a linear relationship between independent variables. This situation causes the variance of the estimations of the model parameters obtained by the Least Squares Estimator method to increase and move away from the true value, resulting in unstable and incorrect results. Biased Estimator methods are developed to eliminate the adverse effects caused by multicollinearity. In this study, a test statistic is obtained to test the significance of the model coefficients for the Liu-Type Estimator using the test statistic method suggested in the study of Halawa and El-Bassiouni 2000. With a simulation study, the significance of the model coefficients of the Ridge, Liu, and Liu type biased estimators in different situations is tested; the type I errors and power values of the estimators are calculated; the results are compared. In addition, a real data application is performed to better understand the test procedure.

Makale detayları

Testing the Significance of Regression Coefficients in Liu Type Estimators

Yazarlar:

Hilal Kaplan Tabak | Meral Ebegil | Esra Gökpınar

Yayıncı:

Gazi University

Ana Başlıklar:

Applied Statistics

Dergi:

Gazi University Journal of Science Vol.37 Issue 4, pp.2062-2083

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331647

pISSN:

2147-1762

Biased estimators

Hypothesis testing

Multicollinearity

Powers of tests

Type I error

Sayı

Makale detayları

Testing the Significance of Regression Coefficients in Liu Type Estimators

Yazarlar: Hilal Kaplan Tabak | Meral Ebegil | Esra Gökpınar

Yayıncı: Gazi University

Ana Başlıklar: Applied Statistics

Dergi: Gazi University Journal of Science Vol.37 Issue 4, pp.2062-2083

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001331647

pISSN: 2147-1762

Biased estimators

Hypothesis testing

Multicollinearity

Powers of tests

Type I error

Sayı

Yazarlar:

Hilal Kaplan Tabak | Meral Ebegil | Esra Gökpınar

Yayıncı:

Gazi University

Ana Başlıklar:

Applied Statistics

Dergi:

Gazi University Journal of Science Vol.37 Issue 4, pp.2062-2083

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331647

pISSN:

2147-1762