This study compares parameter estimation methods for the unit inverse Weibull distribution under ranked set sampling (RSS) and simple random sampling (SRS) techniques. We examine Maximum Product Spacing Estimation, Ordinary Least Squares Estimation, Maximum Likelihood Estimation, Weighted Least Squares Estimation, Anderson-Darling Estimation, Left-Tail Anderson-Darling Estimation, Right-Tail Anderson-Darling Estimation, Cramer-von Mises Estimation, Minimum Spacing Absolute Distance Estimation, Minimum Spacing Square Distance Estimation, Minimum Spacing Absolute-Log Distance Estimation, and Minimum Spacing Square Log Distance Estimation. Monte Carlo simulations evaluate estimator performance using mean squared error (MSE), bias (AB), and mean absolute relative error (MARE). A COVID-19 dataset validates the practical applicability of the methods. Results show RSS-based estimators consistently outperform SRS counterparts across all metrics and estimation techniques. RSS demonstrates superior accuracy, reduced AB and lower MSE, particularly in small sample scenarios. These findings establish RSS as the preferred approach for unit inverse Weibull parameter estimation, providing significant improvements in statistical efficiency and reliability for practical applications.
Public health represents nowadays one of the major global challenges, especially with regards to the predictive analysis of biometric data. In this framework, Body Mass Index (referred to as BMI ) represents one of the most recognized indicators for monitoring, understanding and forecasting the general health of the population. An analysis of BMI could have important social and economic impacts, allowing policy-makers to foster more and more sustainable economic development and to develop preventive strategies (and effective health policies) aimed at promoting social well-being. This contribution proposes the use of computational approaches to model BMI using both traditional statistical methods and AI techniques, namely linear regression, random forest, decision trees and neural networks. In particular, a comparative analysis of the predictive performance of the models mentioned above is proposed by discussing the significance of different health-related indicators on BMI. The computational analysis is conducted on a dataset consisting of health parameters of a sample of women in Belgium collected between 2000 and 2001. The results demonstrate the effectiveness of linear and AI-based in BMI and valuable information for makers interested in quantitative assessment of health parameters and disease prediction to promote strategic choices that can contribute to improving collective well-being and reducing health and economic disparities, generating benefits at both the individual and community levels.
MultiClass Classification (MCC) is a foundational task in machine learning, especially within high-dimensional domains like text classification. While most studies focus on predictive accuracy, the growing demands of large-scale models have raised urgent questions about their environmental cost. In line with the Green AI paradigm, this work examines not only the performance but also the carbon and energy efficiency of various classifier-strategy combinations for MCC. Using two real-world textual datasets we systematically evaluate strategies such as One-Vs-Rest (OVA), One-Vs-One (OVO), Best-of-Best (BOB), and Error-Correcting Output Codes (ECOC), across classifiers ranging from simple Na & imath;ve Bayes to complex Artificial Neural Networks. We introduce emissions-per-accuracy metrics to measure the environmental efficiency of each configuration. Our findings show that while models like Random Forest incur high computational and ecological costs, simpler classifiers such as Logistic Regression and Na & imath;ve Bayes achieve comparable performance with drastically lower emissions. OVA consistently offers the best trade-off between speed and accuracy, while OVO and BOB prove more robust to class imbalance. Notably, Threshold-based Na & imath;ve Bayes paired with OVO demonstrates strong performance and sustainability. By integrating environmental considerations into MCC evaluation, this study highlights the importance of choosing classifier-strategy pairs that are not only effective but also computationally and ecologically responsible.
In this article, we introduce a new three-parameter Frechet distribution via the modified Lehmann Type-II class of distributions and investigate its properties, inferential methods, and real-world application. Fundamental distributional properties such as the quantile function, moments, moment generating function, entropy, and order statistics have been discussed. Inferential results have been obtained within the classical and Bayesian frameworks, utilizing a progressive censoring scheme. In the classical estimation framework, maximum likelihood estimation and maximum product spacing estimation are considered to obtain the estimates using the Newton-Raphson method. In addition, approximate confidence intervals have been derived using maximum likelihood estimation estimates. Meanwhile, we have used informative and non-informative prior via likelihood and product spacing functions to find the Bayes estimates in the Bayesian framework. Since the posterior distributions cannot be expressed in closed form, we employ a combination of Gibbs sampling and the Metropolis-Hastings algorithm to obtain the Bayes estimates. Furthermore, credible intervals are constructed using the Bayes estimates under informative and non-informative Prior. A comprehensive simulation study is carried out to assess the performance of the proposed estimation techniques. To demonstrate the practical utility of the proposed model, a real-life dataset is analyzed, showcasing the effectiveness of the proposed methodologies.
This study aimed to classify the cancer types across different regions in Jordan using a machine learning-algorithm and based on the Decision Tree Analysis (DTA) technique. The model employed patients' demographic information-specifically gender, age, and region of residence-as independent variables (IVs) to assess their interaction with cancer types as the dependent variable (DV). The objective was to determine the predictive relationship between these demographic factors and cancer types to support regional cancer profiling and inform targeted public health planning. The research utilized secondary data from the Ministry of Health, the Directorate of Non-Communicable Diseases, and the Jordan Cancer Registry for the years 2020 to 2021. A total of 9,547 cancer cases were analyzed using the DTA model, which effectively identified significant incidence patterns, with the central region of Jordan accounting for the highest number of cases (n = 6,815; 71.4%). The model classified cancer into 25 distinct types based on demographic attributes, with breast cancer being the most prevalent, particularly among middle-aged females residing in the central region. The DTA model demonstrated high efficacy in handling and stratifying large-scale medical data, predicting cancer type interactions, categorizing and labeling datasets, and suggesting potential category mergers. These findings have important implications for the development of focused cancer prevention strategies and the efficient allocation of healthcare resources. However, a key limitation of the study is the incomplete characterization of cancer patient attributes across all Jordanian regions.
