The KL estimator for the inverse Gaussian regression model
Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the Liu estimators have been developed as an alternative to the MLE. Both estimators possess smaller mean squared error (MSE) o...
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| Published in: | Concurrency and computation Vol. 33; no. 13 |
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| Abstract | Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the Liu estimators have been developed as an alternative to the MLE. Both estimators possess smaller mean squared error (MSE) over the MLE. Recently, Kibria and Lukman developed KL estimator, which was found to outperform the ridge and the Liu estimators in the linear regression model. With this expectation, we developed the KL estimator for the inverse Gaussian regression model. We compare the proposed estimator's performance with some existing estimators in terms of theoretical comparison, the simulation study, and real‐life application. Smaller MSE criterion shows that the proposed estimator with one of its shrinkage parameter performs the best. |
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| AbstractList | Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the Liu estimators have been developed as an alternative to the MLE. Both estimators possess smaller mean squared error (MSE) over the MLE. Recently, Kibria and Lukman developed KL estimator, which was found to outperform the ridge and the Liu estimators in the linear regression model. With this expectation, we developed the KL estimator for the inverse Gaussian regression model. We compare the proposed estimator's performance with some existing estimators in terms of theoretical comparison, the simulation study, and real‐life application. Smaller MSE criterion shows that the proposed estimator with one of its shrinkage parameter performs the best. Abstract Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models. The ridge and the Liu estimators have been developed as an alternative to the MLE. Both estimators possess smaller mean squared error (MSE) over the MLE. Recently, Kibria and Lukman developed KL estimator, which was found to outperform the ridge and the Liu estimators in the linear regression model. With this expectation, we developed the KL estimator for the inverse Gaussian regression model. We compare the proposed estimator's performance with some existing estimators in terms of theoretical comparison, the simulation study, and real‐life application. Smaller MSE criterion shows that the proposed estimator with one of its shrinkage parameter performs the best. |
| Author | Lukman, Adewale F. Ayinde, Kayode Algamal, Zakariya Y. Kibria, B. M. Golam |
| Author_xml | – sequence: 1 givenname: Adewale F. orcidid: 0000-0003-2881-1297 surname: Lukman fullname: Lukman, Adewale F. email: adewale.folaranmi@lmu.edu.ng organization: Landmark University – sequence: 2 givenname: Zakariya Y. orcidid: 0000-0002-0229-7958 surname: Algamal fullname: Algamal, Zakariya Y. organization: University of Mosul – sequence: 3 givenname: B. M. Golam surname: Kibria fullname: Kibria, B. M. Golam organization: Florida International University – sequence: 4 givenname: Kayode surname: Ayinde fullname: Ayinde, Kayode organization: Federal University of Technology |
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| Cites_doi | 10.1080/03610929508831585 10.1080/03610918.2020.1797797 10.1002/cem.3125 10.59170/stattrans-2014-002 10.1081/STA-120019959 10.1080/03610926.2019.1595654 10.1007/s00362-006-0037-0 10.1080/03610929308831027 10.1002/cem.3203 10.1007/BF02595697 10.1080/03610926.2020.1791339 10.1002/0471458503 10.1080/02331888.2011.605891 10.1080/03610926.2018.1481977 10.1080/00949655.2020.1718150 10.1080/25765299.2019.1706799 10.1080/03610920902807911 10.1155/2019/6342702 10.1080/03610918.2012.735317 10.1081/SAC-120017499 10.1080/03610929208830909 10.1155/2020/9758378 10.22237/jmasm/1462075860 10.1080/03610918.2014.995815 |
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| References | 2015; 5 2013; 47 2012 2010 2010; 39 1993; 22 2019; 12 2019; 34 2017; 46 1970; 12 1934; 45 2016; 15 2003; 474 2018; 48 2003; 32 2014; 43 2018; 9 2018; 17 2020 1995; 24 2008; 49 2019b; 2019 1965 2020; 27 2014; 15 2019; 49 2020a 2020b; 90 1992; 21 2019a; 33 2016; 45 2001; 10 e_1_2_6_32_1 e_1_2_6_10_1 e_1_2_6_31_1 Lukman AF (e_1_2_6_15_1) 2017; 46 Malehi AS (e_1_2_6_7_1) 2015; 5 e_1_2_6_19_1 Dorugade AV (e_1_2_6_23_1) 2014; 15 Shamany E. R. (e_1_2_6_8_1) 2019; 12 Ahmad S (e_1_2_6_25_1) 2020 e_1_2_6_13_1 e_1_2_6_36_1 e_1_2_6_11_1 e_1_2_6_33_1 e_1_2_6_17_1 e_1_2_6_18_1 Imran M (e_1_2_6_30_1) 2019; 34 e_1_2_6_16_1 Hardin JW (e_1_2_6_29_1) 2012 Lukman AF (e_1_2_6_34_1) 2018; 17 e_1_2_6_21_1 e_1_2_6_20_1 Ayinde K (e_1_2_6_9_1) 2018; 9 Frisch R (e_1_2_6_12_1) 1934; 45 Lemeshko BY (e_1_2_6_6_1) 2010 Hoerl AE (e_1_2_6_14_1) 1970; 12 e_1_2_6_5_1 e_1_2_6_4_1 Brownlee KA (e_1_2_6_35_1) 1965 e_1_2_6_24_1 e_1_2_6_3_1 e_1_2_6_2_1 e_1_2_6_22_1 e_1_2_6_28_1 e_1_2_6_27_1 e_1_2_6_26_1 |
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| Snippet | Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression models.... Abstract Multicollinearity poses an undesirable effect on the efficiency of the maximum likelihood estimator (MLE) in both Gaussian and non‐Gaussian regression... |
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| SubjectTerms | efficiency inverse Gaussian regression KL estimator Liu estimator Maximum likelihood estimators MLE Regression models ridge |
| Title | The KL estimator for the inverse Gaussian regression model |
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