Developing a Liu‐type estimator in beta regression model
The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the regression coefficients of this model. However, it is known that multicollinearity problem affects badly the variance of ML...
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| Published in: | Concurrency and computation Vol. 34; no. 5 |
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Hoboken, USA
John Wiley & Sons, Inc
28-02-2022
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| Abstract | The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the regression coefficients of this model. However, it is known that multicollinearity problem affects badly the variance of ML estimator. Therefore, this paper introduces the Liu‐type estimator for the beta regression model to handle the multicollinearity problem. The performance of the proposed (Liu‐type) estimator is compared to the ML estimator and other biased (ridge and Liu) estimators depending on the mean squared error (MSE) criterion by conducting a simulation study and through an empirical application. The results indicated that the proposed estimator outperformed the ML, ridge, and Liu estimators. |
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| AbstractList | The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the regression coefficients of this model. However, it is known that multicollinearity problem affects badly the variance of ML estimator. Therefore, this paper introduces the Liu‐type estimator for the beta regression model to handle the multicollinearity problem. The performance of the proposed (Liu‐type) estimator is compared to the ML estimator and other biased (ridge and Liu) estimators depending on the mean squared error (MSE) criterion by conducting a simulation study and through an empirical application. The results indicated that the proposed estimator outperformed the ML, ridge, and Liu estimators. Abstract The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the regression coefficients of this model. However, it is known that multicollinearity problem affects badly the variance of ML estimator. Therefore, this paper introduces the Liu‐type estimator for the beta regression model to handle the multicollinearity problem. The performance of the proposed (Liu‐type) estimator is compared to the ML estimator and other biased (ridge and Liu) estimators depending on the mean squared error (MSE) criterion by conducting a simulation study and through an empirical application. The results indicated that the proposed estimator outperformed the ML, ridge, and Liu estimators. |
| Author | Abonazel, Mohamed R. Algamal, Zakariya Yahya |
| Author_xml | – sequence: 1 givenname: Zakariya Yahya orcidid: 0000-0002-0229-7958 surname: Algamal fullname: Algamal, Zakariya Yahya email: zakariya.algamal@uomosul.edu.iq organization: University of Mosul – sequence: 2 givenname: Mohamed R. orcidid: 0000-0001-6010-001X surname: Abonazel fullname: Abonazel, Mohamed R. email: mabonazel@cu.edu.eg organization: Cairo University |
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| References | 2010; 34 2015; 56 2006; 11 2019; 1 1993; 22 2013; 42 2017; 46 2008; 35 2020; 13 2020; 34 2014; 84 2021; 1‐13 2019; 1341 2018; 42 2003; 32 2007; 36 2012; 53 2021; 1‐15 2021; 1‐16 2021; 91 2021; 14 2004; 33 2021; 16 2004; 31 2019; 60 2019; 81 2018b; 183 2018a; 11 2018; 117 2021 1970a; 12 2015; 136 2019a 2019; 89 2008; 49 2020; 90 2020; 49 2018 2018; 52 2020; 44 1970b; 12 2021; 1‐26 2016; 45 e_1_2_8_28_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 Hoerl AE (e_1_2_8_2_1) 1970; 12 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 Hoerl AE (e_1_2_8_3_1) 1970; 12 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 Rady EA (e_1_2_8_29_1) 2019 e_1_2_8_32_1 e_1_2_8_11_1 e_1_2_8_51_1 Noeel F (e_1_2_8_34_1) 2021; 14 e_1_2_8_30_1 e_1_2_8_25_1 Mahmood SW (e_1_2_8_37_1) 2020; 13 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 Algamal ZY (e_1_2_8_24_1) 2018; 11 Dawoud I (e_1_2_8_48_1) 2021; 1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_50_1 |
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| Snippet | The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used... Abstract The beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML)... |
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| SubjectTerms | beta regression model biased estimation Liu‐type estimator Maximum likelihood estimators multicollinearity Regression coefficients Regression models Spanish football league |
| Title | Developing a Liu‐type estimator in beta regression model |
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