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Papers by Nasir Elmesmari
Mağallaẗ al-ʿulūm al-insāniyyaẗ wa-al-ṭabīʿiyyaẗ, Mar 1, 2024
Scholars journal of physics, mathematics and statistics, Nov 24, 2022
Original Research Article We examine the effect of unnecessary differencing (over-differencing) o... more Original Research Article We examine the effect of unnecessary differencing (over-differencing) on the appropriateness of the proposed models (Autoregressive of order one AR(1), Autoregressive of order two AR(2), and Moving Average of order two MA(2)). Our interest arises from the fact that in practical applications the fitted model due to inappropriately differenced data can still suitably describe the data sample based on the goodness of fit test using residual analysis. Given that we use simulation study to detect the consequences of unnecessary differencing on the fitted model. While the simulation study can be controlled using different scenarios, it becomes more challenging when dealing with real data. The validity and performance of the fitted models was checked by observing the changes in the estimated coefficients, the associated standard errors (SE), the residual variance, and Akaike information (AIC) by comparing them with the true parameters of the system (true model). The uniqueness of this paper is to examine how the fitted model is sensitive (valid) to the over-differencing.
Biomedical Sciences Department. They provided me with constructive comments, which greatly improv... more Biomedical Sciences Department. They provided me with constructive comments, which greatly improved the quality of my dissertation. I would also like to thank Dr. Yeong C. Kim and Dr. San Ming Wang from the University of Nebraska Medical Center for providing me with the data used in this research. My thanks also goes to Dr. Xijin Ge for helping me get the data in this research. I would also thank Dr. Emhimad Abdalla from the University of Wisconsin for his constructive comments and his help with the Fortran programs. iv My special thanks goes to the Ministry of Higher Education, Libya, for sponsoring my Ph.D. study abroad through the Libyan North American Scholarship Program (LNASP). I am also indebted to Dr. Mohamed. M. Mekaeil for his superb mentoring. Last but not least, my sincere thanks to my family, especially to my mother, father, wife, brothers, and sons, for their love, sacrifices, and encouragement that helped me to complete my research. Finally, I must say that in the journey of my life, I am indebted to so many family members, friends, and well-wishers who have provided invaluable advice during uncertain and difficult times, and have helped me keep my dream alive. I wish I could thank every person but, nevertheless, they are always in my heart. v TABLE OF CONTENTS LIST OF FIGURES .
Scholars journal of physics, mathematics and statistics, Nov 11, 2021
Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for esti... more Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for estimating joint posterior distributions in Bayesian analysis. The Markov chain Monte Carlo technique has been used in order to estimate the model parameters based on the different prior distributions. MCMC simulations were carried out in order to evaluate the linear mixed model using different parameters of the prior distribution. In this paper, we established the linear mixed model with different types of variables. The proposed parameters of the prior distribution are different from the traditional parameters of the prior distribution. We assumed special parameters for the prior distribution based on some background or information about the data science. This work aims to estimate the parameters using a point estimator or find a confidence interval (credible interval) for the unknown parameters. Also, a specific hypothesis about these parameters can be tested using a random sample from the posterior distribution. The performance of each prior is measured based on the effective sample size (ESS) for the estimated model. The results showed that the estimated linear mixed model with proposed parameters of the prior distribution performed very well in comparison with the standard or traditional prior (inverse-Wishart prior for random effect component). Based on the scale reduction factors, the estimated model with proposed parameters performed better in comparison with scale reduction factors for the traditional model.
Scholars journal of physics, mathematics and statistics, Nov 24, 2022
Original Research Article We examine the effect of unnecessary differencing (over-differencing) o... more Original Research Article We examine the effect of unnecessary differencing (over-differencing) on the appropriateness of the proposed models (Autoregressive of order one AR(1), Autoregressive of order two AR(2), and Moving Average of order two MA(2)). Our interest arises from the fact that in practical applications the fitted model due to inappropriately differenced data can still suitably describe the data sample based on the goodness of fit test using residual analysis. Given that we use simulation study to detect the consequences of unnecessary differencing on the fitted model. While the simulation study can be controlled using different scenarios, it becomes more challenging when dealing with real data. The validity and performance of the fitted models was checked by observing the changes in the estimated coefficients, the associated standard errors (SE), the residual variance, and Akaike information (AIC) by comparing them with the true parameters of the system (true model). The uniqueness of this paper is to examine how the fitted model is sensitive (valid) to the over-differencing.
Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for esti... more Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for estimating joint posterior distributions in Bayesian analysis. The Markov chain Monte Carlo technique has been used in order to estimate the model parameters based on the different prior distributions. MCMC simulations were carried out in order to evaluate the linear mixed model using different parameters of the prior distribution. In this paper, we established the linear mixed model with different types of variables. The proposed parameters of the prior distribution are different from the traditional parameters of the prior distribution. We assumed special parameters for the prior distribution based on some background or information about the data science. This work aims to estimate the parameters using a point estimator or find a confidence interval (credible interval) for the unknown parameters. Also, a specific hypothesis about these parameters can be tested using a random sample from th...
Given the growing importance of data stewardship in today’s digital economy, the ability to bette... more Given the growing importance of data stewardship in today’s digital economy, the ability to better manage vulnerabilities associated with electronic data is of interest to organizational leadership. Human error is a vulnerability that increases the likelihood of electronic data risk, such as the threat of a data breach. One countermeasure against human error is the ability to measure human intent toward compliance with an information assurance (IA) policy, as one input for better managing the human factor within an organization. While large organizations are likely to have access to resources for managing the human factor, small to mid-size organizations are less likely to have access to similar resources. Thus, this paper explores the use of commonly available research tools to provide a poor man’s countermeasure for better managing the threat/vulnerability pair that is electronic data risk/human error. Our methodology uses logistic regression to evaluate the statistical significan...
THRESHOLD MODELS FOR GENOME-WIDE ASSOCIATION MAPPING OF FAMILIAL BREAST CANCER INCIDENCE IN HUMANS
Mağallaẗ al-ʿulūm al-insāniyyaẗ wa-al-ṭabīʿiyyaẗ, Mar 1, 2024
Scholars journal of physics, mathematics and statistics, Nov 24, 2022
Original Research Article We examine the effect of unnecessary differencing (over-differencing) o... more Original Research Article We examine the effect of unnecessary differencing (over-differencing) on the appropriateness of the proposed models (Autoregressive of order one AR(1), Autoregressive of order two AR(2), and Moving Average of order two MA(2)). Our interest arises from the fact that in practical applications the fitted model due to inappropriately differenced data can still suitably describe the data sample based on the goodness of fit test using residual analysis. Given that we use simulation study to detect the consequences of unnecessary differencing on the fitted model. While the simulation study can be controlled using different scenarios, it becomes more challenging when dealing with real data. The validity and performance of the fitted models was checked by observing the changes in the estimated coefficients, the associated standard errors (SE), the residual variance, and Akaike information (AIC) by comparing them with the true parameters of the system (true model). The uniqueness of this paper is to examine how the fitted model is sensitive (valid) to the over-differencing.
Biomedical Sciences Department. They provided me with constructive comments, which greatly improv... more Biomedical Sciences Department. They provided me with constructive comments, which greatly improved the quality of my dissertation. I would also like to thank Dr. Yeong C. Kim and Dr. San Ming Wang from the University of Nebraska Medical Center for providing me with the data used in this research. My thanks also goes to Dr. Xijin Ge for helping me get the data in this research. I would also thank Dr. Emhimad Abdalla from the University of Wisconsin for his constructive comments and his help with the Fortran programs. iv My special thanks goes to the Ministry of Higher Education, Libya, for sponsoring my Ph.D. study abroad through the Libyan North American Scholarship Program (LNASP). I am also indebted to Dr. Mohamed. M. Mekaeil for his superb mentoring. Last but not least, my sincere thanks to my family, especially to my mother, father, wife, brothers, and sons, for their love, sacrifices, and encouragement that helped me to complete my research. Finally, I must say that in the journey of my life, I am indebted to so many family members, friends, and well-wishers who have provided invaluable advice during uncertain and difficult times, and have helped me keep my dream alive. I wish I could thank every person but, nevertheless, they are always in my heart. v TABLE OF CONTENTS LIST OF FIGURES .
Scholars journal of physics, mathematics and statistics, Nov 11, 2021
Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for esti... more Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for estimating joint posterior distributions in Bayesian analysis. The Markov chain Monte Carlo technique has been used in order to estimate the model parameters based on the different prior distributions. MCMC simulations were carried out in order to evaluate the linear mixed model using different parameters of the prior distribution. In this paper, we established the linear mixed model with different types of variables. The proposed parameters of the prior distribution are different from the traditional parameters of the prior distribution. We assumed special parameters for the prior distribution based on some background or information about the data science. This work aims to estimate the parameters using a point estimator or find a confidence interval (credible interval) for the unknown parameters. Also, a specific hypothesis about these parameters can be tested using a random sample from the posterior distribution. The performance of each prior is measured based on the effective sample size (ESS) for the estimated model. The results showed that the estimated linear mixed model with proposed parameters of the prior distribution performed very well in comparison with the standard or traditional prior (inverse-Wishart prior for random effect component). Based on the scale reduction factors, the estimated model with proposed parameters performed better in comparison with scale reduction factors for the traditional model.
Scholars journal of physics, mathematics and statistics, Nov 24, 2022
Original Research Article We examine the effect of unnecessary differencing (over-differencing) o... more Original Research Article We examine the effect of unnecessary differencing (over-differencing) on the appropriateness of the proposed models (Autoregressive of order one AR(1), Autoregressive of order two AR(2), and Moving Average of order two MA(2)). Our interest arises from the fact that in practical applications the fitted model due to inappropriately differenced data can still suitably describe the data sample based on the goodness of fit test using residual analysis. Given that we use simulation study to detect the consequences of unnecessary differencing on the fitted model. While the simulation study can be controlled using different scenarios, it becomes more challenging when dealing with real data. The validity and performance of the fitted models was checked by observing the changes in the estimated coefficients, the associated standard errors (SE), the residual variance, and Akaike information (AIC) by comparing them with the true parameters of the system (true model). The uniqueness of this paper is to examine how the fitted model is sensitive (valid) to the over-differencing.
Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for esti... more Original Research Article Markov chain Monte Carlo (MCMC) is the most widely used method for estimating joint posterior distributions in Bayesian analysis. The Markov chain Monte Carlo technique has been used in order to estimate the model parameters based on the different prior distributions. MCMC simulations were carried out in order to evaluate the linear mixed model using different parameters of the prior distribution. In this paper, we established the linear mixed model with different types of variables. The proposed parameters of the prior distribution are different from the traditional parameters of the prior distribution. We assumed special parameters for the prior distribution based on some background or information about the data science. This work aims to estimate the parameters using a point estimator or find a confidence interval (credible interval) for the unknown parameters. Also, a specific hypothesis about these parameters can be tested using a random sample from th...
Given the growing importance of data stewardship in today’s digital economy, the ability to bette... more Given the growing importance of data stewardship in today’s digital economy, the ability to better manage vulnerabilities associated with electronic data is of interest to organizational leadership. Human error is a vulnerability that increases the likelihood of electronic data risk, such as the threat of a data breach. One countermeasure against human error is the ability to measure human intent toward compliance with an information assurance (IA) policy, as one input for better managing the human factor within an organization. While large organizations are likely to have access to resources for managing the human factor, small to mid-size organizations are less likely to have access to similar resources. Thus, this paper explores the use of commonly available research tools to provide a poor man’s countermeasure for better managing the threat/vulnerability pair that is electronic data risk/human error. Our methodology uses logistic regression to evaluate the statistical significan...
THRESHOLD MODELS FOR GENOME-WIDE ASSOCIATION MAPPING OF FAMILIAL BREAST CANCER INCIDENCE IN HUMANS