Emad ElDin Abdel-Salam Soliman Hegazy

@kau.edu.sa

department of Statistics, Faculty of Science, Kind AbdulAziz university, Jeddah, Ksa
Associate professor of Statistics in King AbulAziz university

Emad ElDin Abdel-Salam Soliman Hegazy


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https://researchid.co/emadsalam

EDUCATION

PhD in Statistics, Cairo university, Egypt

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Modeling and Simulation
5

Scopus Publications

53

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • An effectiveness study of the Bayesian inference with multivariate autoregressive moving average processes
    Mohammed Albassam, Emad E. A. Soliman, Sherif S. Ali
    Communications in Statistics Simulation and Computation, 2023
    Multivariate time series may be found in many fields of application such as economics, meteorology, and utilities. In economics, for example, one may record yearly money supply and real interest rate. These variables are modeled, and the parameters are estimated jointly to understand the nature of the dynamic relationships between variables and increase the precisions of the estimates. Better estimates can be achieved when the series are modeled jointly if there is information on one series contained in the others. Shaarawy has introduced an analytical approximate Bayesian methodology for the statistical inference of multivariate autoregressive moving average (VARMA) processes. The main objective of the current study is to investigate the numerical effectiveness of his proposed methodology in solving the estimation problems of multivariate VARMA processes by conducting a wide simulation study. Moreover, the study investigates the sensitivity of the numerical effectiveness of the proposed methodology with respect to the parameters’ values and time series length. Simulation results showed that the methodology succeeded in estimating the parameters of VARMA models, for all parameters’ values and time series lengths.
  • Bayesian Estimation of Multivariate Pure Moving Average Processes
    Mohammed Albassam, Emad E. A. Soliman, Sherif S. Ali
    IEEE Access, 2022
    The multivariate estimation problems arise if the observations are available for several related variables of interest. The multivariate time series may be found in many fields of application such as economics, meteorology and utilities. The current study has three main objectives. The first one is to develop an approximate convenient Bayesian methodology to estimate the parameters of multivariate moving average processes. The second objective is to investigate the numerical efficiency of the proposed technique in solving the estimation problems by conducting a wide simulation study. The last objective is to study the sensitivity of the numerical efficiency with respect to the parameters values and time series length. The results show that the proposed technique succeeded in estimating the parameters of the multivariate moving average processes. The results are not sensitive to the changes in parameter values or in time series length.
  • Mixed integer nonlinear goal programming approach to variable selection in linear regression
    Mohammed Albassam, Ali El Hefnawy, Emad E. A. Soliman
    Communications in Statistics Simulation and Computation, 2021
    Several methods have been proposed in regression analysis for variable selection. Such methods enable researchers to distinguish between significant and insignificant variables, thus they provide a parsimonious regression model based solely on significant variables. Classical methods for variable selection include forward selection, backward elimination, and stepwise regression. The use of these methods is restricted by some strict assumptions about the given data. In the current study a new Mixed Integer Nonlinear Goal Programing model, which dispenses with these rigid assumptions, is introduced. The new model is less sensitive to outliers and allows incorporating any necessary restrictions on the model’s parameters. The performance of the new model is compared with the Classical methods using large scale simulation studies. The evaluation of the proposed method and the Classical methods will be done based on the proportion of correct selection of the significant independent variables.
  • On bayesian identification of autoregressive processes
    Emad El-Din Abdel-Salam Soliman, Samir Mostafa Shaarawy, Waseem W. Sorour
    Pakistan Journal of Statistics and Operation Research, 2015
    The main objective of the current study is to handle the identification problem of autoregressive processes from the Bayesian point of view. Two Bayesian identification approaches are considered. They are referred to as the direct and the indirect approaches. The two approaches are employed to solve the Bayesian identification problem of autoregressive processes using three well known priors. These priors are the G prior, the Natural-Conjugate prior and Jeffrey's prior. The theoretical derivations related to the two Bayesian identification approaches are conducted using the above mentioned priors. Moreover, the performance of the two techniques, using each of the three priors, is investigated via comprehensive simulation studies. Simulation results show that the two techniques are adequate to solve the identification problem of autoregressive processes. The increase in the time series length leads to better performance for each technique. The use of different priors doesn't affect the previous results.
  • Bayesian identification of moving average models
    Samir M. Shaarawy, Emad E. A. Soliman, Sherif S. Ali
    Communications in Statistics Theory and Methods, 2007
    This study approaches the Bayesian identification of moving average processes using an approximate likelihood function and a normal gamma prior density. The marginal posterior probability mass function of the model order is developed in a convenient form. Then one may investigate the posterior probabilities over the grid of the order and choose the order with the highest probability to solve the identification problem. A comprehensive simulation study is carried out to demonstrate the performance of the proposed procedure and check its adequacy in handling the identification problem. In addition, the proposed Bayesian procedure is compared with some non Bayesian automatic techniques and another Bayesian technique. The numerical results support the adequacy of using the proposed procedure in solving the identification problem of moving average processes.

RECENT SCHOLAR PUBLICATIONS

  • Assessment of the performance of the Bayesian Forecasting for Vector ARMA Processes
    EEA Soliman, SS Ali
    SCOPUA Journal of Applied Statistical Research 2 (1) , 2026
    2026
  • Bayesian Identification of Seasonal Vector ARMA Processes
    EEA Shaarawy, S.M., Ali, S.S. and Soliman
    The Egyptian Statistical Journal 68 (2), 129-146 , 2024
    2024
    Citations: 2
  • An effectiveness study of the Bayesian inference with multivariate autoregressive moving average processes
    M Albassam, EEA Soliman, SS Ali
    Communications in statistics-Simulation and Computation 52 (10), 4773-4788 , 2023
    2023
    Citations: 4
  • A Direct Bayesian Methodology to Identify the Order of Moving Average Processes using Different Prior Distributions
    MS Al Bassam, EEA Soliman, SS Ali
    The Egyptian Statistical Journal 66 (2), 1-16 , 2022
    2022
    Citations: 3
  • Bayesian estimation of multivariate pure moving average processes
    M Albassam, EEA Soliman, SS Ali
    IEEE Access 10, 14225-14235 , 2022
    2022
    Citations: 4
  • Mixed integer nonlinear goal programming approach to variable selection in linear regression
    M Albassam, A El Hefnawy, EEA Soliman
    Communications in Statistics-Simulation and Computation 50 (12), 4028-4040 , 2021
    2021
    Citations: 1
  • A Bayesian Algorithm to Identify the Orders of Vector Moving-Average Processes with Seasonality
    ASSSEEA Shaarawy SM
    The Egyptian Statistical Journal 64 (No.1), 1-20 , 2021
    2021
    Citations: 4
  • Indirect and Direct Bayesian Techniques to Identify The orders of Vector ARMA Processes
    SS Shaarawy, S.M., Soliman, E.E.A. and Ali
    The Egyptian Statistical Journal 62 (No.1), 15-34 , 2018
    2018
    Citations: 1
  • Bayesian Prediction of Moving Average Processes using Different Types of Priors
    SH Shaarawy SM, Soliman EEA
    The Egyptian Statistical Journal 62 (1), 35-52 , 2018
    2018
    Citations: 1
  • Estimating the Orders of Bivariate Mixed ARMA(p,q) Processes using Bayesian Approach
    SE E A
    The Egyptian Statistical Journal 60 (1), 1-19 , 2016
    2016
  • On bayesian identification of autoregressive processes
    EEDAS Soliman, SM Shaarawy, WW Sorour
    Pakistan Journal of Statistics and Operation Research, 11-28 , 2015
    2015
    Citations: 2
  • On Bayesian Identification of Autoregressive Processes.
    SM Shaarawy, EEA Soliman, WW Sorour
    Pakistan Journal of Statistics & Operation Research 11 (1) , 2015
    2015
  • BTS 1.0 : A Computer Package to do a Complete Bayesian Analysis of Autoregressive Processes
    SS Assas, B.M., Shaarawy, S.M., Soliman E.E.A. and Ali
    The Egyptian Statistical Journal 59 (2), 213-236 , 2015
    2015
  • Selection of Prior Distribution in the Indirect Bayesian Identification of Moving Average Models
    SS Albassam, M.S., Soliman E.E.A. and Ali
    Canadian Journal on Computing in Mathematics, Natural Sciences, Engineering … , 2013
    2013
  • Bayesian identification of seasonal moving average models
    RM Shaarawy, S.M., Soliman, E.E.A. and El-Souda
    The Egyptian Statistical Journal 55 (1), 40-52 , 2011
    2011
    Citations: 3
  • Bayesian prediction of autoregressive models using different types of priors
    H Shaarawy, S.M., Soliman, E.E.A. and Shahin
    The Egyptian Statistical Journal 54 (2), 108-126 , 2010
    2010
    Citations: 3
  • On Bayesian identification of moving average models
    AM Soliman, E.E.A. and Abdel Fattah
    The Egyptian Statistical Journal 53 (2), 106-124 , 2009
    2009
  • Efficiency of the approximate Bayesian prediction of ARMA models: A simulation study
    SE E A
    The Egyptian Statistical Journal 52 (2), 56-75 , 2008
    2008
  • Bayesian identification of moving average models
    SM Shaarawy, EEA Soliman, SS Ali
    Communications in Statistics—Theory and Methods 36 (12), 2301-2312 , 2007
    2007
    Citations: 17
  • Direct and indirect Bayesian identification of seasonal autoregressive models
    RM Soliman, E.E.A. and El-Souda
    The Ninteenth Annual Conference on Statistics and Modeling in Human and … , 2007
    2007

MOST CITED SCHOLAR PUBLICATIONS

  • Bayesian identification of moving average models
    SM Shaarawy, EEA Soliman, SS Ali
    Communications in Statistics—Theory and Methods 36 (12), 2301-2312 , 2007
    2007
    Citations: 17
  • On direct and indirect Bayesian identification of autoregressive models
    AL Daif, EA Soliman, SS Ali
    The 14th Annual Conference on Statistics and Computer Modeling in Human and … , 2003
    2003
    Citations: 8
  • An effectiveness study of the Bayesian inference with multivariate autoregressive moving average processes
    M Albassam, EEA Soliman, SS Ali
    Communications in statistics-Simulation and Computation 52 (10), 4773-4788 , 2023
    2023
    Citations: 4
  • Bayesian estimation of multivariate pure moving average processes
    M Albassam, EEA Soliman, SS Ali
    IEEE Access 10, 14225-14235 , 2022
    2022
    Citations: 4
  • A Bayesian Algorithm to Identify the Orders of Vector Moving-Average Processes with Seasonality
    ASSSEEA Shaarawy SM
    The Egyptian Statistical Journal 64 (No.1), 1-20 , 2021
    2021
    Citations: 4
  • A Direct Bayesian Methodology to Identify the Order of Moving Average Processes using Different Prior Distributions
    MS Al Bassam, EEA Soliman, SS Ali
    The Egyptian Statistical Journal 66 (2), 1-16 , 2022
    2022
    Citations: 3
  • Bayesian identification of seasonal moving average models
    RM Shaarawy, S.M., Soliman, E.E.A. and El-Souda
    The Egyptian Statistical Journal 55 (1), 40-52 , 2011
    2011
    Citations: 3
  • Bayesian prediction of autoregressive models using different types of priors
    H Shaarawy, S.M., Soliman, E.E.A. and Shahin
    The Egyptian Statistical Journal 54 (2), 108-126 , 2010
    2010
    Citations: 3
  • Bayesian Identification of Seasonal Vector ARMA Processes
    EEA Shaarawy, S.M., Ali, S.S. and Soliman
    The Egyptian Statistical Journal 68 (2), 129-146 , 2024
    2024
    Citations: 2
  • On bayesian identification of autoregressive processes
    EEDAS Soliman, SM Shaarawy, WW Sorour
    Pakistan Journal of Statistics and Operation Research, 11-28 , 2015
    2015
    Citations: 2
  • Mixed integer nonlinear goal programming approach to variable selection in linear regression
    M Albassam, A El Hefnawy, EEA Soliman
    Communications in Statistics-Simulation and Computation 50 (12), 4028-4040 , 2021
    2021
    Citations: 1
  • Indirect and Direct Bayesian Techniques to Identify The orders of Vector ARMA Processes
    SS Shaarawy, S.M., Soliman, E.E.A. and Ali
    The Egyptian Statistical Journal 62 (No.1), 15-34 , 2018
    2018
    Citations: 1
  • Bayesian Prediction of Moving Average Processes using Different Types of Priors
    SH Shaarawy SM, Soliman EEA
    The Egyptian Statistical Journal 62 (1), 35-52 , 2018
    2018
    Citations: 1
  • Assessment of the performance of the Bayesian Forecasting for Vector ARMA Processes
    EEA Soliman, SS Ali
    SCOPUA Journal of Applied Statistical Research 2 (1) , 2026
    2026
  • Estimating the Orders of Bivariate Mixed ARMA(p,q) Processes using Bayesian Approach
    SE E A
    The Egyptian Statistical Journal 60 (1), 1-19 , 2016
    2016
  • On Bayesian Identification of Autoregressive Processes.
    SM Shaarawy, EEA Soliman, WW Sorour
    Pakistan Journal of Statistics & Operation Research 11 (1) , 2015
    2015
  • BTS 1.0 : A Computer Package to do a Complete Bayesian Analysis of Autoregressive Processes
    SS Assas, B.M., Shaarawy, S.M., Soliman E.E.A. and Ali
    The Egyptian Statistical Journal 59 (2), 213-236 , 2015
    2015
  • Selection of Prior Distribution in the Indirect Bayesian Identification of Moving Average Models
    SS Albassam, M.S., Soliman E.E.A. and Ali
    Canadian Journal on Computing in Mathematics, Natural Sciences, Engineering … , 2013
    2013
  • On Bayesian identification of moving average models
    AM Soliman, E.E.A. and Abdel Fattah
    The Egyptian Statistical Journal 53 (2), 106-124 , 2009
    2009
  • Efficiency of the approximate Bayesian prediction of ARMA models: A simulation study
    SE E A
    The Egyptian Statistical Journal 52 (2), 56-75 , 2008
    2008