A New Two-Parameter Estimator for the Poisson Regression Model

Asar, Yasin; Genç, Aşır

doi:10.1007/s40995-017-0174-4

Cited by 38 publications

(47 citation statements)

References 16 publications

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“…We adopted the aircraft damage data to evaluate the proposed estimator's performance and some other estimators in this study. The dataset was initially used by Myers et al 21 and recently by Asar and Genc 19 and others. The dataset provides the information about two types of aircraft, the McDonnell Douglas A-4 Skyhawk and the Grumman A-6 Intruder.…”

Section: Real Life Applicationmentioning

confidence: 99%

“…The explanatory variables are as follows: x 1 is a binary variable representing the aircraft type (A-4 coded as 0 and A-6 coded as 1), x 2 and x 3 denote bomb load in tons and total months of aircrew experience, respectively. The response variable, y represents the number of locations with damage on the aircraft, which follows a Poisson distribution 19 , 21 . Amin et al 28 examine if the model follows a Poisson regression model by adopting the Pearson chi-square goodness of fit test.…”

Section: Real Life Applicationmentioning

confidence: 99%

“…Özkale and Kaciranlar 8 combine the Liu regression estimator and the ridge regression to form the two-parameter estimator in LRM. Thus, Asar and Genc 19 implemented the two-parameter estimator to the Poisson regression model. Rashad and Algamal 20 developed a new ridge estimator for the Poisson regression model by modifying Poisson modified jackknifed ridge regression.…”

Section: Introductionmentioning

confidence: 99%

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A new estimator for the multicollinear Poisson regression model: simulation and application

Lukman

Adewuyi

Månsson

et al. 2021

Sci Rep

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The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new estimator with some biasing parameters to estimate the regression coefficients for the PRM when there is multicollinearity problem. Some simulation experiments are conducted to compare the estimators' performance by using the mean squared error (MSE) criterion. For illustration purposes, aircraft damage data has been analyzed. The simulation results and the real-life application evidenced that the proposed estimator performs better than the rest of the estimators.

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Section: Real Life Applicationmentioning

confidence: 99%

Section: Real Life Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new estimator for the multicollinear Poisson regression model: simulation and application

Lukman

Adewuyi

Månsson

et al. 2021

Sci Rep

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show abstract

“…Månsson and Shukur ( 2011 ) proposed a Poisson ridge regression estimator (PRRE) to reduce the effects of problems associated with multicollinear data. Kibria et al ( 2015 ) proposed a number of biasing parameters, and Asar and Genç ( 2018 ) suggested a two-parameter biased estimator in the PRM to adjust for multicollinearity. Türkan and Özel ( 2016 ) developed Almost Unbiased PRRE (AUPRRE) and Modified AUPPRE (MAUPRRE).…”

Section: Introductionmentioning

confidence: 99%

Biased Adjusted Poisson Ridge Estimators-Method and Application

Qasim

Månsson

Amin

et al. 2020

Iran J Sci Technol Trans Sci

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Månsson 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, Türkan and Özel (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 ($$ \hat{k}_{q4} $$ k ^ q 4 ) performed better than the rest in the presence of high to strong (0.80–0.99) multicollinearity situation.

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“…In regression modeling, data in the form of counts are usually common. Count data regression modeling has received much attention in medicine, behavioral sciences, psychology, and econometrics [1,2,3,36]. The Poisson and negative binomial regression models are the most basic models under count data regression models [4] (Wang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Algamal

2019

Stat., optim. inf. comput.

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Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Count data regression modeling has received much attention in several science fields in which the Poisson and negative binomial regression models are the most basic models. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, firefly algorithm is proposed to perform variable selection for count data regression models. Extensive simulation studies and two real data applications are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.

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A New Two-Parameter Estimator for the Poisson Regression Model

Cited by 38 publications

References 16 publications

A new estimator for the multicollinear Poisson regression model: simulation and application

A new estimator for the multicollinear Poisson regression model: simulation and application

Biased Adjusted Poisson Ridge Estimators-Method and Application

Variable Selection in Count Data Regression Model based on Firefly Algorithm

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