Modified jackknife ridge estimator for the Conway-Maxwell-Poisson model

Algamal, Zakariya Yahya; Abonazel, Mohamed R.; Awwad, Fuad A.; Eldin, Elsayed Tag

doi:10.1016/j.sciaf.2023.e01543

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…As a result, they showed that their proposed estimator improved the performance of the ridge estimator. Algamal et al [23] proposed a modified jackknife ridge estimator for the COMP model. As a result, they found that their proposed estimator had minimum bias and minimum mean square error.…”

Section: Introductionmentioning

confidence: 99%

A New Effective Jackknifing Estimator in the Negative Binomial Regression Model

Koç,

Koç

2023

Symmetry

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The negative binomial regression model is a widely adopted approach when dealing with dependent variables that consist of non-negative integers or counts. This model serves as an alternative regression technique for addressing issues related to overdispersion in count data. Typically, the maximum likelihood estimator is employed to estimate the parameters of the negative binomial regression model. However, the maximum likelihood estimator can be highly sensitive to multicollinearity, leading to unreliable results. To eliminate the adverse effects of multicollinearity in the negative binomial regression model, we propose the use of a jackknife version of the Kibria–Lukman estimator. In this study, we conducted a theoretical comparison between the proposed jackknife Kibria–Lukman negative binomial regression estimator and several existing estimators documented in the literature. To assess the performance of the proposed estimator, we conducted two simulation studies and performed a real data application. The results from both the simulation studies and the real data application consistently demonstrated that the proposed jackknife Kibria–Lukman negative binomial regression estimator outperforms other estimators.

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Section: Introductionmentioning

confidence: 99%

A New Effective Jackknifing Estimator in the Negative Binomial Regression Model

Koç,

Koç

2023

Symmetry

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“…Secondly, multicollinearity shows a wider confidence interval which has the tendency to produce a false negative result, known as an error of omission in hypothesis testing (Qasim et al 2020). Some researchers have come with diverse way to contend with multicollinearity in linear regression Model (LRM) see E.G., Hoerl & Kennard (1970), Liu (2003), Kibria & Lukman (2020), Ozkale & Kaciranlar (2007), Suhail et al, (2020), Ugwuowo et al (2021), , Perveen & Suhail, (2021), Babar et al, (2021), , Algamal et al (2023), Wasim et al, (2023), Abonazel et al (2023) and . Hoerl & Kennard (1970) proposed the ridge estimator to handle high correlation in the LRM.…”

Section: Introductionmentioning

confidence: 99%

Selection of A New Biasing Parameter for the Jackknife Kibria-Lukman Estimator for the Negative Binomial Regression Model

et al. 2023

Preprint

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The negative binomial regression model (NBRM) is a generalized linear model which relaxes the restrictive assumption by the Poisson regression model when the variance is equal to the mean. The estimation of the parameters of the NBRM is obtained using the maximum likelihood (ML) method. Maximum likelihood estimator becomes unstable when the explanatory variables are linearly dependent, a situation known as multicollinearity. Based on this, we developed a new estimator called modified jackknifed Negative Binomial Kibria-Lukman (MJNBKL) estimator for the radiation of multicollinearity in NBRM using four different biasing (shrinkage) parameters. We establish superiority condition for MJNBKL estimator over the ones. The performance MJNBKL estimator was ascertained by comparing it with the existing ones through a Monte Carlo simulation study and two real life application datasets. The results of the simulation and real life application show that MJNBKL estimator outperformed the other estimators compared with by having the smallest MSE across all sample sizes and for different levels of correlation for the four biasing parameters used and the third biasing parameter is the optimal shrinkage parameter with the lowest MSE.

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