The beta Liu-type estimator: simulation and application

Ali Erkoç; Esra Ertan; Zakariya Yahya Algamal; Kadri Ulaş Akay

Makale detayları

The beta Liu-type estimator: simulation and application

Yazarlar:

Ali Erkoç | Esra Ertan | Zakariya Yahya Algamal | Kadri Ulaş Akay

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.828-840

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290022

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 The Beta Regression Model BRM is commonly used while analyzing data where the dependent variable is restricted to the interval 0,1 for example proportion or probability. The Maximum Likelihood Estimator MLE is used to estimate the regression coefficients of BRMs. But in the presence of multicollinearity, MLE is very sensitive to high correlation among the explanatory variables. For this reason, we introduce a new biased estimator called the Beta Liu-Type Estimator BLTE to overcome the multicollinearity problem in the case that dependent variable follows a Beta distribution. The proposed estimator is a general estimator which includes other biased estimators, such as the Ridge Estimator, Liu Estimator, and the estimators with two biasing parameters as special cases in BRM. The performance of the proposed new estimator is compared to the MLE and other biased estimators in terms of the Estimated Mean Squared Error EMSE criterion by conducting a simulation study. Finally, a numerical example is given to show the benefit of the proposed estimator over existing estimators.

 The Beta Regression Model BRM is commonly used while analyzing data where the dependent variable is restricted to the interval 0,1 for example proportion or probability. The Maximum Likelihood Estimator MLE is used to estimate the regression coefficients of BRMs. But in the presence of multicollinearity, MLE is very sensitive to high correlation among the explanatory variables. For this reason, we introduce a new biased estimator called the Beta Liu-Type Estimator BLTE to overcome the multicollinearity problem in the case that dependent variable follows a Beta distribution. The proposed estimator is a general estimator which includes other biased estimators, such as the Ridge Estimator, Liu Estimator, and the estimators with two biasing parameters as special cases in BRM. The performance of the proposed new estimator is compared to the MLE and other biased estimators in terms of the Estimated Mean Squared Error EMSE criterion by conducting a simulation study. Finally, a numerical example is given to show the benefit of the proposed estimator over existing estimators.

 The Beta Regression Model BRM is commonly used while analyzing data where the dependent variable is restricted to the interval 0,1 for example proportion or probability. The Maximum Likelihood Estimator MLE is used to estimate the regression coefficients of BRMs. But in the presence of multicollinearity, MLE is very sensitive to high correlation among the explanatory variables. For this reason, we introduce a new biased estimator called the Beta Liu-Type Estimator BLTE to overcome the multicollinearity problem in the case that dependent variable follows a Beta distribution. The proposed estimator is a general estimator which includes other biased estimators, such as the Ridge Estimator, Liu Estimator, and the estimators with two biasing parameters as special cases in BRM. The performance of the proposed new estimator is compared to the MLE and other biased estimators in terms of the Estimated Mean Squared Error EMSE criterion by conducting a simulation study. Finally, a numerical example is given to show the benefit of the proposed estimator over existing estimators.

 The Beta Regression Model BRM is commonly used while analyzing data where the dependent variable is restricted to the interval 0,1 for example proportion or probability. The Maximum Likelihood Estimator MLE is used to estimate the regression coefficients of BRMs. But in the presence of multicollinearity, MLE is very sensitive to high correlation among the explanatory variables. For this reason, we introduce a new biased estimator called the Beta Liu-Type Estimator BLTE to overcome the multicollinearity problem in the case that dependent variable follows a Beta distribution. The proposed estimator is a general estimator which includes other biased estimators, such as the Ridge Estimator, Liu Estimator, and the estimators with two biasing parameters as special cases in BRM. The performance of the proposed new estimator is compared to the MLE and other biased estimators in terms of the Estimated Mean Squared Error EMSE criterion by conducting a simulation study. Finally, a numerical example is given to show the benefit of the proposed estimator over existing estimators.

Makale detayları

The beta Liu-type estimator: simulation and application

Yazarlar:

Ali Erkoç | Esra Ertan | Zakariya Yahya Algamal | Kadri Ulaş Akay

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.828-840

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290022

pISSN:

2651-477X

Beta regression model

Liu-type estimator

maximum likelihood estimator

multicollinearity

Sayı

Makale detayları

The beta Liu-type estimator: simulation and application

Yazarlar: Ali Erkoç | Esra Ertan | Zakariya Yahya Algamal | Kadri Ulaş Akay

Yayıncı: Hacettepe University

Ana Başlıklar: Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.828-840

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001290022

pISSN: 2651-477X

Beta regression model

Liu-type estimator

maximum likelihood estimator

multicollinearity

Sayı

Yazarlar:

Ali Erkoç | Esra Ertan | Zakariya Yahya Algamal | Kadri Ulaş Akay

Yayıncı:

Hacettepe University

Ana Başlıklar:

Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.52 Issue 3, pp.828-840

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290022

pISSN:

2651-477X