Awwad, F. A., B. J. Francis, and M. R. Abonazel, "Down syndrome, temporal variation and fallout radiation revisited: statistical evidence", Commun. Math. Biol. Neurosci., vol. 2021, pp. Article–ID, 2021. Abstract
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El-Masry, A. M., A. H. Youssef, and M. R. Abonazel, "Using logit panel data modeling to study important factors affecting delayed completion of adjuvant chemotherapy for breast cancer patients", Commun. Math. Biol. Neurosci., vol. 2021, pp. Article–ID, 2021. Abstract
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Dawoud, I., and M. R. Abonazel, "Robust Dawoud–Kibria estimator for handling multicollinearity and outliers in the linear regression model", Journal of Statistical Computation and Simulation, vol. 91, no. 17: Taylor & Francis, pp. 3678–3692, 2021. Abstract
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Abonazel, M. R., S. M. El-sayed, and O. M. Saber, "Performance of robust count regression estimators in the case of overdispersion, zero inflated, and outliers: simulation study and application to German health data", Commun. Math. Biol. Neurosci., vol. 2021, pp. Article–ID, 2021. Abstract
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Abonazel, M. R., and O. Shalaby, "On Labor Productivity in OECD Countries: Panel Data Modeling", WSEAS TRANSACTIONS on BUSINESS and ECONOMICS, vol. 18, 2021. Abstract

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Abonazel, M. R., F. A. Awwad, A. F. Lukman, I. B. Lekara-Bayo, E. Y. Atanu, and others, "Long-run determinants of Nigerian inflation rate: ARDL bounds testing approach", WSEAS Transactions on Business and Economics, vol. 18: WSEAS, pp. 1370–1379, 2021. Abstract
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Youssef, A. H., A. R. Kamel, and M. R. Abonazel, "Robust SURE estimates of profitability in the Egyptian insurance market", Statistical journal of the IAOS, vol. 37, no. 4: IOS Press, pp. 1275–1287, 2021. Abstract
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Lukman, A. F., B. Aladeitan, K. Ayinde, and M. R. Abonazel, "Modified ridge-type for the Poisson regression model: simulation and application", Journal of Applied Statistics: Taylor & Francis, pp. 1-13, 2021. AbstractWebsite

The Poisson regression model (PRM) is employed in modelling the relationship between a count variable (y) and one or more explanatory variables. The parameters of PRM are popularly estimated using the Poisson maximum likelihood estimator (PMLE). There is a tendency that the explanatory variables grow together, which results in the problem of multicollinearity. The variance of the PMLE becomes inflated in the presence of multicollinearity. The Poisson ridge regression (PRRE) and Liu estimator (PLE) have been suggested as an alternative to the PMLE. However, in this study, we propose a new estimator to estimate the regression coefficients for the PRM when multicollinearity is a challenge. We perform a simulation study under different specifications to assess the performance of the new estimator and the existing ones. The performance was evaluated using the scalar mean square error criterion and the mean squared error prediction error. The aircraft damage data was adopted for the application study and the estimators’ performance judged by the SMSE and the mean squared prediction error. The theoretical comparison shows that the proposed estimator outperforms other estimators. This is further supported by the simulation study and the application result.

Awwad, F. A., M. A. Mohamoud, and M. R. Abonazel, "Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling", PLOS ONE, vol. 16, no. 4: Public Library of Science, pp. 1-16, 04, 2021. AbstractWebsite

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

Farghali, R. A., M. Qasim, G. B. M. Kibria, and M. R. Abonazel, "Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application", Communications in Statistics - Simulation and Computation: Taylor & Francis, pp. 1-16, 2021. AbstractWebsite

AbstractIn this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.