The Egyptian Statistical Journal

Eid Al-fitr Mubarak

The Egyptian Statistical Journal is indexed in several databases.

A Proposed Ordinal Logistic Regression Model for the Mental Health Status Changes in Egypt According to the COVID-19 Pandemic

Authors:
Mariam N. Abd El-Azim, Mohamed R. Abonazel, El-Sayed Khater, Mohammed Atta

Abstract:
The global health emergency of COVID-19, stemming from the novel coronavirus SARS-CoV-2, emerged toward the end of 2019 and spread rapidly worldwide, profoundly affecting societies. The rapid transmission of the virus transcended geographical borders, social structures, and economic systems, resulting in unprecedented disruptions across various dimensions of human life. COVID-19 has had a profound effect on the global health system as the virus has resulted in significant morbidity and mortality. The pandemic has led to a surge in mental health challenges worldwide, including increased rates of anxiety, depression, and stress among populations. This study employs an ordinal logistic model to examine factors associated with mental health during the pandemic in Egypt and assess their significance across multiple dimensions. Utilizing data from the Combined COVID-19 MENA Monitor Household Survey (CCMMHH). The study examined various factors influencing mental health in Egypt, highlighting significant associations between these variables and mental well-being. With every year of age, higher income, and higher education levels are associated with better mental health. Moreover, individuals who are less concerned about infection and the pandemic's economic impacts are more likely to have better mental health. With the effective implementation of social distancing strategies, individuals without coping strategies have lower odds for specific mental health categories. Finally, effective implementation of social distancing strategies was associated with better mental health.




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Credit Scoring in Digital Lending: A Stepwise Logistic Regression and Weight of Evidence Approach in Kenya

Authors:
Omukami Howard, Cynthia Mwau, Denis Kuria Wangui, Andrew Mbuya

Abstract:
Mobile money lending has grown rapidly in recent years, with one of the major challenges being the determination of borrowers' creditworthiness, a classification problem complicated by credit risk and the choice of appropriate scoring models. Credit scoring, which uses statistical analysis of historical borrower data to estimate repayment likelihood, remains a critical tool for financial institutions in assessing lending risk. This study evaluates borrower creditworthiness using historical data from Mobipesa Limited, a digital lending company based in Nairobi, Kenya. Data were collected from 495 borrowers over one year, comprising 33 predictor variables (32 numeric and one categorical). Stepwise logistic regression was used to reduce variables and automatically obtain an optimal model. The dataset was split into 70% training and 30% testing sets. Using R, the training data were binned and transformed using the weight-of-evidence (WoE) transformation, after which probabilities of default (PD) for individual borrowers were computed from the cumulative distribution function of the standard logistic model. The PDs represent the likelihood of repayment for each borrower. The developed model achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.825, reflecting 82.5% accuracy in distinguishing reliable borrowers from potential defaulters on the Mobipesa platform. These findings enabled the company to refine its borrower database, inform the public on credit approval thresholds, and strengthen credit policy. Moreover, other firms in digital lending ecosystems can draw on these insights to improve credit-scoring models and variable-selection approaches, thereby enhancing overall lending efficiency and risk management.
https://esju.journals.ekb.eg/article_477895.html




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Hybridizing GARCH and Random Forest for Enhanced Modeling of Volatility Clustering

Authors:
Sohair F. Higazi, Sara F. Aboud, Hani A. Khedr

Abstract:
Clustered volatility refers to a non-stationary process in which the mean and variance of a time series change over time rather than remaining constant, reflecting the dynamic nature of volatility, which fluctuates in response to varying market or structural conditions and often results in alternating periods of high and low volatility. It can be identified through visual inspection of the time series and formal autocorrelation tests using the Autocorrelation Function, where significant correlations at multiple lags indicate that current volatility depends on past volatility, meaning that large or small past movements are likely to be followed by similarly large or small movements regardless of direction. This study investigates the enhancement of the Autoregressive Conditional Heteroskedasticity (ARCH) model and its generalization, the GARCH model, through integration with Random Forest (RF) machine learning techniques. Using daily historical stock prices of Apple Inc. from January 2020 to July 2025, focusing on the closing price, four models were evaluated: ARCH, GARCH, and their Random Forest–enhanced versions. Traditional models showed weak performance, with ARCH producing the highest errors and GARCH performing slightly better, while hybrid models significantly improved results. GARCH with Random Forest explained approximately 49.3% of the variance, whereas ARCH with Random Forest achieved the best performance, with very low errors and explanatory power of 97.82%. These findings indicate that traditional models have limited predictive capability. In contrast, hybrid models provide more accurate, adaptive, and dynamic forecasts, highlighting the potential of combining econometric models with machine learning to enhance the precision of volatility prediction in financial markets.





https://esju.journals.ekb.eg/article_473814.html

Geospatial Modelling of Overdispersed Zero-inflated Count Data: An Application to COVID-19 Mortalities across the United States

Authors:
Amira S. Elayouty, Doaa E. Sakr, Abdelnaser S. Abdrabou, Mohamed A. Ismail

Abstract:
In the early stages of any pandemic, the number of cases/deaths related to the contagious infectious disease in a certain area is often skewed, over-dispersed, and has excessive zeros, as some areas may have no contagions for some time. For these reasons, this paper adopted a geographically weighted zero-inflated negative binomial regression (GWZINBR). This model extends the Zero Inflated Negative Binomial (ZINBR) model to the context of Geographically Weighted Regression (GWR). The GWR models proved useful at modelling data recorded across space, where response-predictor relationships are likely to be non-stationary across the study region. We aim here at modelling the number of COVID-19 deaths across the United States counties and the spatial variability in the impacts of the potential socio-economic and environmental risk factors using a version of GWR that accommodates the special characteristics of the data. This GWZINBR simultaneously relaxes the assumption of stationary relationships and accounts for overdispersion and excess zero counts in the response. The results of the GWZINBR model are compared to those of the global negative binomial and ZINBR models, and provide a better fit to the data relative to all other fitted models.


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