Mahmoud A. Abdel-Fattah

The pooled ridge-Liu estimator (PRLE) has been recently suggested to address issues arising from ill-conditioning situations in the beta regression (BR) model. The PRLE has been shown to enhance the model’s stability and efficiency compared to the maximum-likelihood estimator (MLE), ridge estimator (RE), and Liu estimator (LE), under mild conditions. This paper introduces a new jackknifed pooled ridge-Liu estimator (JPRLE) designed to reduce the mean square error (MSE) associated with the PRLE. The proposed JPRLE combines the MLE, the jackknifed ridge estimator (JRE), and the jackknifed Liu estimator (JLE) as special cases. Nonlinear programming models are recommended for selecting the tuning parameters (TPs) of both the PRLE and the JPRLE. Superiority conditions for the new JPRLE are demonstrated theoretically according to squared bias and MSE matrix criteria. The proposed estimator was motivated by two real applications to the heat-treating test and the body fat data files. The findings of this paper suggest that the proposed JPRLE is a promising candidate for enhancing the efficiency of the ill-conditioned BR model when compared to the MLE, RE, LE, PRLE, JRE, and JLE.

Background Medical students are the future doctors and play an essential role in the management of health issues. Their understanding of Alzheimer’s disease (AD) is not only required but also necessary to provide the best possible care to patients. The present study aimed to assess medical students’ knowledge about AD within the context of the recent reform of the Egyptian medical educational system, which switched to competency-based instead of outcome-based education since 2017.
Methods A descriptive cross-sectional study was conducted among medical students in public and private Egyptian medical schools. Between August and November 2024, an anonymous self-administered questionnaire was uploaded to Google Forms and distributed online through commonly used social media platforms. The Alzheimer’s Disease Knowledge Scale (ADKS), a validated and reliable tool, was used for the measurement of AD-related knowledge. Univariate and multivariable logistic regression models were used to determine the factors associated with having good or poor knowledge about AD among participants.
Results In total, 1100 medical students were included through convenience and snowball sampling methods; their mean age was 20.9 ± 1.9 years, 55.5% were males, 59.6% were in their clinical years, and 15.6% had a positive family history of AD. The students’ mean knowledge score was 19.10 ± 2.96 out of 30, representing 63.7% of answers correct, with a range of scores between 9 and 29. About 70.8% of the sample had good knowledge. The highest percentage of correct answers was for the treatment and management domain (76.5%), while the lowest percentage was for the caregiving domain (52.2%). Predictors of good knowledge were females [adjusted odds ratio (aOR) = 1.33 (95% confidence interval (CI): 1.01–1.76, p = 0.043], attending a public university [aOR = 1.62 (95% CI: 1.09–2.41), p = 0.015), clinical year students [aOR = 1.53 (95% CI: 1.07–2.16), p = 0.018], living in an urban area [(aOR = 1.67 (95% CI: 1.23–2.25), p < 0.001], and having higher family monthly income [aOR = 1.75 (95% CI: 1.13–2.72), p = 0.012].
Conclusions The study highlights gaps in Egyptian medical students’ knowledge of AD. Knowledge gaps were found in domains of caregiving and risk factors, urging educators and policymakers to enhance curricula, particularly preclinical curricula, with a specific focus on some socio-economic determinants.

This paper is about developing a new class of two-parameter shrinkage estimators for the binomial model under the multicollinearity problem. The proposed class includes the ridge estimator (RE) and the maximum likelihood estimator (MLE) as special cases. The necessary and sufficient conditions that ensure the superiority of the proposed estimator over the MLE and the RE in terms of the mean squared error (MSE) matrix were obtained. The performance of new estimators is evaluated and compared with the MLE and some REs through simulation under moderate to strong correlations. A real application supports the simulation results.

Inflation is a major economic problem in emerging market economies and requires accurate models to avoid high volatility and long periods of inflation. This paper is aimed at evaluating a Functional Time Series (FTS) model as compared to other models in forecasting inflation in Uganda. The monthly Time Series (TS) data for the general Consumer Price Index (CPI) was used during the period of Jul-2005 to Jun-2020. Box-Jenkins’ Auto Regressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methodologies are explored to evaluate the FTS method of forecasting the general CPI where their accuracies are compared and validated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Existing inflation models in Uganda are outdated by structural changes in the economy igniting the need for a novel accurate model for forecasting inflation. FTS technique is overall considered accurate and particularly used to model high-frequency data such as Uganda general CPI data modeled as a functional observation after smoothing by kernel smoothing methods compared to traditional methods. Business operations and consumers normally base their decisions on modeled and forecasted inflation with their decisions affected by inflation uncertainties that hinder their motivations to invest and save in a given country as they try to avoid inflation-related risks. Findings therefore show FTS having great accuracies and recommended the method for forecasting Uganda inflation. This opens a new framework for extending the Box and Jenkin’s methodology to the functional setting.