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1.	Qasim, Muhammad, et al. (författare) Biased Adjusted Poisson Ridge Estimators-Method and Application 2020 Ingår i: Iranian Journal of Science and Technology Transaction A: Science. - : Springer. - 1028-6276 .- 2364-1819. ; 44, s. 1775-1789 Tidskriftsartikel (refereegranskat)abstract Mansson and Shukur (Econ Model 28:1475-1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Turkan and Ozel (J Appl Stat 43:1892-1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ((k) over cap (q4)) performed better than the rest in the presence of high to strong (0.80-0.99) multicollinearity situation.

Qasim, Muhammad, et al. (författare)
Biased Adjusted Poisson Ridge Estimators-Method and Application
2020
Ingår i: Iranian Journal of Science and Technology Transaction A: Science. - : Springer. - 1028-6276 .- 2364-1819. ; 44, s. 1775-1789
Tidskriftsartikel (refereegranskat)abstract
- Mansson and Shukur (Econ Model 28:1475-1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Turkan and Ozel (J Appl Stat 43:1892-1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ((k) over cap (q4)) performed better than the rest in the presence of high to strong (0.80-0.99) multicollinearity situation.

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