Predicting clinical outcomes in liver cirrhosis using machine learning and data balancing technique
Liver cirrhosis is a chronic and life-threatening disease that significantly impacts liver function and overall patient health. Early prediction of clinical outcomes in cirrhotic patients can aid in timely intervention and improved treatment planning. In this study, a dataset containing real-world clinical, biochemical, and demographic data from cirrhosis patients was used to develop predictive models […]
Topp-Leone modified Kies-G family of distributions: Properties, actuarial measures, inference and applications
We introduce a new two-parameter generalized family of distributions named the Topp-Leone Modified Kies-G family by combining the Topp-Leone-G and Modified Kies-G families. Several statistical properties of the proposed family were derived, including the moments, moment-generating function, order statistics, entropy, mean deviation, measures of inequality, and actuarial measures. A baseline distribution called the Topp-Leone Modified […]
A Novel Robust M-Estimator for the Random-Coefficients Regression Model: Simulation and Its Application in Energy Management Systems
A random coefficient regression (RCR) model refers to a statistical model that incorporates random coefficients into degradation models, typically assumed to be normally distributed. An RCR model is a special type of panel data model. The RCR model provides a wide range of consequences for situations involving decision-making difficulties. The classical estimation methods for the […]
Type II half logistic Ailamujia distribution with numerical illustrations to medical data
In this article, we introduce a new two-parameter distribution called the type II half-logistic Ailamujia distribution, which is constructed from the type II half-logistic-G family and Ailamujia distribution. The probability density function exhibits decreasing, unimodal, and right-skewed shapes, whereas the hazard rate function shows decreasing, J-shaped, increasing, and inverted trends. Several statistical characteristics for the […]
Improving geographically weighted Poisson regression model based on metaheuristic algorithms: Application to cancer rate data
Geographically weighted Poisson regression (GWPR) model is a further refinishing of Poisson regression for model the spatial count data and consider local association of variables. Nevertheless, the GWPR model faces several challenges that can impact its effectiveness and reliability. One of these challenges is the bandwidth selection. An improper bandwidth value results to either fitting […]