Dina abdelhady | Faculity Of Commerce (original) (raw)

Papers by Dina abdelhady

NILES journal for Geriatric and Gerontology

Background: geriatric persons are more likely to be frail and have less resilience to psychologic... more Background: geriatric persons are more likely to be frail and have less resilience to psychological stressors. Objectives: to determine elders vulnerability during COVID-19 pandemic. Methods: Predefined questionnaires were fulfilled by 500 participants from various governorates in Egypt. The survey consists of four tools including: COVID-19 anxiety scale, COVID-19 Coping inventory (C-19C), The State-Trait Anxiety Inventory, State version (STAIS-Anxiety scale) and Coping Responses inventory (CRI). One way ANOVA test was conducted to compare the effects of age on different aspects of anxiety and coping related to COVID-19 pandemic followed by a Tukey post hoc test to make pair wise comparisons between group means. Results: The elders group (aged ≥ 60 years) consisted of 24 males and 46 females. In comparison to younger age groups, older females had the highest scores in the COVID-19 anxiety scale (M= 61.565, p-value < 0.001) and the lowest scores in different subscales of both coping scales. This was shown in the coping responses inventory (M= 99.369, p <0.01) with seven out of its subscales and COVID-19 coping Inventory with three of its subscales. While, older males had the lowest scores in coping strategies inventory (M= 103.75, p-value <0.05) with all of its 16 subscales except for the emotional discharge subscale and in the COVID-19 coping Inventory (M= 8.10870, p-value .005) with 2 of its subscales. However, they didn't have statistically significant increase in COVID-19 anxiety pandemic. Conclusion: COVID-19 pandemic has a major psychological impact on the society with the greatest burden among elders females.

International journal of academic research, Jan 30, 2014

المجلة العلمیة للدراسات والبحوث المالیة والتجاریة, 2022

A new method for adding parameters to a well-established distribution to obtain more flexible new... more A new method for adding parameters to a well-established distribution to obtain more flexible new families of distributions is applied to Lomax distribution (LD). This method is known as the Alpha-Power transformation (APT) and introduced by Mahdavi and Kundu (2015). The reliability and statistical properties of the proposed models are studied. Maximum likelihood method of estimation is utilized to obtain the estimators of the population parameters. The extended model is applied on a real data and the results are given and compared to other models. Also, a bivariate distribution was constructed from the proposed distribution using the Farlie-Gumbel-Morgenstern (FGM) copula. Some characteristics of this distribution and the estimation using the maximum likelihood are shown on a real data.

Middle East Current Psychiatry

Background The COVID-19 Life Events-Anxiety Inventory (C-19LAI) is a newly developed tool and the... more Background The COVID-19 Life Events-Anxiety Inventory (C-19LAI) is a newly developed tool and the only Arabic tool for assessing and measuring anxiety related to different life events during the COVID-19 pandemic. The aim of the study was to test the validity and reliability of this newly designed tool. We used a cross sectional validation multiphasic study and applied the tool on 500 subjects together with the State-Trait Anxiety Inventory (STAI). Results The COVID-19 Life Events-Anxiety Inventory (C-19LAI) showed validity of 73.6% and sensitivity of 85.2%, with acceptable reliability of α = 0.815 and 0.947, respectively. The Life Events Scale and Anxiety Scale of the C-19LAI correlated significantly (p ≤ 0.01) with the State-Trait Anxiety Inventory (r = 0.289 and r = 0.407, respectively). Conclusion The COVID-19 Life Events-Anxiety Inventory (C-19LAI) Scale is a reliable and valid scale that can measure anxiety and events related to anxiety during the COVID 19 pandemic.

New parameters can be introduced to expand families of distributions for added flexibility or to ... more New parameters can be introduced to expand families of distributions for added flexibility or to construct covariate models and this could be done in various ways. In this article, we generalize the Laplace distribution using the quadratic rank transmutation map studied by Shaw et al. (2007) to develop a transmuted Laplace distribution (TLD). We provide a comprehensive description of the mathematical properties of the subject distribution along with its reliability behavior. To show that the TLD distribution can be a better model than one based on the LD distribution we use a real data set of number of million revolutions before failure for each of the 23 ball bearings in the life tests and The usefulness of the transmuted Laplace distribution for modeling reliability data is illustrated.

Annals of Data Science, 2020

In this article, we introduce inverse Power Gompertz distribution with three parameters. Some sta... more In this article, we introduce inverse Power Gompertz distribution with three parameters. Some statistical properties are presented such as hazard rate function, quartile, probability weighted (moments), skewness, kurtosis, entropies function, Bonferroni and Lorenz curves and order statistics. The model parameters are estimated by the method of maximum likelihood, least squares, weighted least squares and Cramérvon Mises. Further, Monte Carlo simulations are carried out to compare between all methods. Finally, the extended model is applied on a real data and the results are given and compared to other models.

Advances and Applications in Statistics, 2019

Journal of Statistics: Advances in Theory and Applications, 2016

The health research has become increasingly reliant on statistical modelling techniques to assess... more The health research has become increasingly reliant on statistical modelling techniques to assess the effect of new health programs, the impact of risk factors on disease, the effects of health behaviours, and a host of other health concerns. Clinical researchers conduct studies about diagnostic tests mainly for MONA N. ABDEL BARY et al. 68 the purpose of either estimating the diagnostic accuracy of a test according to different patient or environmental characteristics or comparing diagnostic accuracy of different tests according to different patient or environmental characteristics. Studies are required to develop robust statistical methods to analyze data from diagnostic studies and assess the properties of available statistical methods. Multiple analytical statistical methods are available to analyze ordinal data. These methods can be a model based approach, such as models for cumulative response probabilities or a non-model based approach, such as a nonparametric method based on ranking. A common model based method used to analyze ordinal data is an ordinal logistic regression. In addition to statistical models, several machine learning algorithms are also available to analyze ordinal data, such as an artificial neural network model, a decision tree model, and a support vector machine model. The current study compares the performance of ordinal logistic regression model with artificial neural networks models for prediction of chronic kidney disease. The results of the current study show outstanding performance of artificial neural networks models for the prediction of the level of chronic kidney disease. In addition, the study illustrates the most affected variables are gender, surgical operations, the blood pressure and potassium ratio. There are many previous studies focus on demonstrated the ability of ANN models in applications binary classifications but through the current study we use neural networks models in multiple classifications. The results of the current study show the following: successful of ANN models in the process of separating and classifying accuracy rate of almost 100%, and the ordinal regression model succeeded in identifying risk factors for renal failure moral influence on the regression model.

Recently it is observed that the generalized exponential distribution can be used quite effective... more Recently it is observed that the generalized exponential distribution can be used quite effectively to analyze lifetime data in one dimension. This paper extends Marshall and Olkin's bivariate exponential model to the Generalized Bivariate Rayleigh (GBR) Distribution. The cumulative distribution function, the probability density function and the conditional distribution of the BGR distribution are reached. The maximum likelihood estimation procedure is derived for the estimation of the GBR parameters when all parameters are unknown and also obtain the observed Fisher information matrix. A special case of the distribution of the GBR distribution is reached in a closed form when one of the parameters is known. Simulation study were analyzed, and a numerical comparison is made between the proposed estimation procedure of the GBR distribution and Block-Basu estimation technique.

Discriminant analysis and logistic regression; shares a common model which is "the general l... more Discriminant analysis and logistic regression; shares a common model which is "the general linear model". These two statistical classification approaches tend to concentrate on the parameter values and their significance level as a guide to the adequacy of the model. Data mining tools have been used for classification and prediction of group membership. Data mining techniques such as neural networks, genetic algorithm, CART, CHAID, Exhaustive CHAID, and QUEST are data-driven rather than model-driven. The study applies Exhaustive CHAID and CART decision tree methods to Small Industrial Businesses data to discover any latent relationship between the financial status of ISB (solvent vs. Insolvent) and some running cost obligations, that include cost of marketing, transportation, raw material, social security and Insurance, and wages. Using "age of Business" as covariate, MANCOVA and ANOVA reveal no significance differences between the two classified groups, and the ...

Pakistan Journal of Statistics and Operation Research, 2013

Regression analysis depends on several assumptions that have to be satisfied. A major assumption ... more Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is never satisfied when variables are from contiguous observations is the independence of error terms. Spatial analysis treated the violation of that assumption by two derived models that put contiguity of observations into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those who make less than 1$ daily). Predictors are some demographic indicators. Explanatory Spatial Data Analysis (ESDA) is performed to examine the existence of spatial clustering and spatial autocorrelation between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between locations. Three statistical models are applied to the data, the Ordinary Least Square regression model (OLS), the Spatial Error Model (SEM) and the Spatial Lag Model (SLM).The Likelihood Ratio test and some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be better than the SLM model. Recommendations are drawn regarding the two spatial models used.

Pakistan Journal of Statistics and Operation Research, 2012

The main objective of this study is to pinpoint the main factors that affect the percentage who s... more The main objective of this study is to pinpoint the main factors that affect the percentage who suffers of malnutrition in developing countries. Three locations are randomly chosen: Asia, Africa, and Middle east and North Africa (MENA); A total of 96 countries were chosen randomly from 137 developing countries of the three locations; and were cross classified by "Location" and "Human Development Index (HDI) as high, middle, and low (UNDP, 2005) i. Data for the study was compiled from FAO (2005) ii. The analysis started with seven explanatory variables and the dependent variable; however, stepwise regression reveals that the average Protein intake and Infant mortality rate were the only two significant variables. "Location and "HDI" are dummy coded and OLS regression is performed using the two significant variables, but the only significant variable was the "average protein intake". OLS multiple regression Model is reapplied to the data using dummy variables technique with interaction with the "average Protein intake", nine regression equations were reached. The Linear Mixed effect Models are also applied, using "location" as the random factor and "HDI" as the fixed factor. Five models were applied: (1) a null model (baseline model)where no predictors are introduced to the model; (2) the fixed model: where predictors used are the covariate and the HDI; (3) the random model: where predictors used are the covariate and Location; (4) the mixed model: where predictors used are the covariate and the HDI I (fixed) and the location (random); and (5) the random coefficient model: where predictors used are the covariate, the HDI Index and the location but produces different prediction equations that differ in slopes and intercepts. Models are compared based on information criterions. The random coefficient model produces the least criterion values and thus fits better than all previous ones. A comparison between the Random Coefficient model results and GLM model is made, and conclusions are reached.

Financial log-returns suffer from volatility clustering; that causes positive autocorrelation coe... more Financial log-returns suffer from volatility clustering; that causes positive autocorrelation coefficients of squared returns, with a relatively slowly decreasing pattern starting from a first small value. Volatility measures the -dispersion‖ not the direction of data points. In this study, the ARCH model and some GARCH family models, namely, GARCH, TGARCH and EGARCH models are discussed applied to data. Steps to conduct tests and methodologies for the applications for each model are given. While ARCH model allows the conditional variance to change over time as a function of past errors leaving the unconditional variance constant, GARCH model specifies the conditional variance to be a linear combination of (q) lags of the squared residuals, TGARCH and EGARCH models allow for leverage effect. The Daily logreturns of three pharmaceutical companies registered in Egypt Stock Market covering the period from 2th January 2000 to 23th Jul 2008 were used for the application of the three models. Results show that the OLS regression model for Arab Drugs Company is sufficient since no serial correlation and no ARCH effect is depicted in the data. For Alex Pharma the best fit model is TGARCH (p,q) model while for EPICO Company, the best fit model is EGARCH (p ,q). The effect of the lags (p ,q) on the fitness of the model were studied for one company, it has been found that fitness is better as q increases and p=1, and as p increases and q=1 and for p>1 fitness is improved for q≤ p.

Several statistical models are used in medical science applications. In the present study, the bi... more Several statistical models are used in medical science applications. In the present study, the binary logistic regression model is used to pin point the significant risk factors affecting the occurrence of a second myocardial infarction (MI). The model is applied to 1500 patients who were initially treated for first MI and have been followed up after at least two years from the first MI treatment, and occurrence of a second MI was observed. Several demographic and medical covariates were used in the analysis. The probability of occurrence, odds ratio for having or not having a second MI is obtained. Results show that three risk factors affect the second occurrence of myocardial infarction, these factors are family history, congestive heart failure, and smoking. Analysis was performed also for males and for females. Same risk factors were reached for males, but only the later two factors were reached for females. Odds ratios for males with "congestive heart failure" are susceptible to a second MI from 14 times to 72 times, while females susceptibility ranges from 14 to 247 times compared to those who did not experience a second MI. The interaction between Dina Hassan Abdel Hady 70 gender and family history proved to be a significance risk factor. Classification tables show that correct classification is approximately 93% for the three analyses, and ROC curves exhibit acceptable classification. The study recommends the study of more factors to the analysis such as treatment types at first MI, building up a database for MI patients, and conducting an awareness health program for patients who have had a first MI.

Discriminant analysis and logistic regression; shares a common model which is "the general linear... more Discriminant analysis and logistic regression; shares a common model which is "the general linear model". These two statistical classification approaches tend to concentrate on the parameter values and their significance level as a guide to the adequacy of the model. Data mining tools have been used for classification and prediction of group membership. Data mining techniques such as neural networks, genetic algorithm, CART, CHAID, Exhaustive CHAID, and QUEST are datadriven rather than model-driven. The study applies Exhaustive CHAID and CART decision tree methods to Small Industrial Businesses data to discover any latent relationship between the financial status of ISB (solvent vs. Insolvent) and some running cost obligations, that include cost of marketing, transportation, raw material, social security and Insurance, and wages. Using "age of Business" as covariate, MANCOVA and ANOVA reveal no significance differences between the two classified groups, and the assumptions for linear discriminant and logistic regression were not satisfied. The Exhaustive CHAID and CART decision trees were applied for the classification of "Insolvent" ISBs with equal priors to discover any unobvious and hidden relationships between the financial status of ISBs and the predictors as categorized by the decision tree algorithms. SPSS software produces different rules to classify each ISB as Solvent or Insolvent, they produce also different classifiers. The Gini measure is used as a splitting criterion for classifiers. Applying Exhaustive CHAID, the Gini measures select the predictors in the following order: Marketing cost, Social security cost, and transportation cost. The splits are all significant at α=5%. When applying CART, the Gini measures bi-split the predictors in the order of: raw Material cost, Marketing cost, Transportation cost, Social security cost, Annual Taxes and monthly wages. Misclassification rate is approximately equal for the two methods