Inference for Generalized Progressive Hybrid Type-II Censored Weibull Lifetimes under Competing Risk Data
The competing risks model plays a pivotal role in the analysis of various domains, including engineering, econometrics, and biology. When a product being tested is likely to fail due to multiple factors, these factors conflict with each other to precipitate product failure. This situation is referred to as the competing risks problem. This paper specifically […]
Different Approaches for Outlier Detection in Life Testing Scenarios
Sometimes the data to be analyzed is not complete, and this may be due to censoring. There are two type of censoring, namely Type I and Type II. Whether the censoring was intentional or accidental, it is no guarantee that the data does not include suspected observations (too small or too large). Theses suspected observations […]
A Novel Version of Geometric Distribution: Method and Application
This paper introduces a new family of discrete distributions, and investigates some of their statistical properties. The geometric distribution is utilized as a baseline for this new family, resulting in the derivation of a new discrete distribution, termed the generalized geometric distribution. This new distribution exhibits a wider range of shapes in its probability mass […]
An Improved Ant Colony Optimization to Uncover Customer Characteristics for Churn Prediction
Customer churn prediction is a critical task in the telecommunication (telecom) industry, where accurate identification of customers at risk of churning plays a vital role in reducing customer attrition. Feature selection (FS) is an integral part in Machine Learning (ML) models which aims to improve performance and reduce computational time (CT). This work optimizes Ant […]
A New Shifted Lomax-X Family of Distributions : Properties and Applications to Actuarial and Financial Data
The Lomax distribution, which is often used to describe severe losses and financial risks because of its heavy tail features, is the basis distribution of the shifted Lomax (SHL-X) family of distributions that we propose in this study. The main objective is to increase the adaptability and accuracy of the traditional Lomax model in the […]
Latent Gaussian Approach to Joint Modelling of Longitudinal and Mixture Cure Outcomes
Joint modelling has become pervasive in analysing data from survival and longitudinal studies. There are several technqiues on joint analyses of datasets from both studies simultaneously. Our interest here is on approximate Bayesian inference using latent Gaussian models (LGMs) to analyse longitudinal and mixture cure outcomes with shared random effect. Longitudinal outcome was modelled using […]
Point and Interval Estimation of Reliability and Entropy for Generalized Exponential Distribution under Generalized Type-II Hybrid Censoring Scheme
In this research paper, the estimation of reliability and entropy measures are analyzed within the context of a generalized Type-II hybrid censoring scheme (GT2HCS) The analysis is conducted while considering a generalized exponential distribution as the underlying lifetime distribution. The study specifically emphasizes the calculation of both Shannon and Renyi entropy measures. Maximum likelihood estimation […]
Leveraging Data Mining for Inference and Prediction in Lung Cancer Research
Lung cancer~is the second most common cancer worldwide, with an estimated 2.21 million new diagnoses and 1.8 million deaths in 2020, according to WHO. Successful lung cancer treatment, early detection, and diagnosis improve survival rates. This study included 270 lung cancer patients and 39 with no lung cancer patients. Logistic regression will be used to […]
Statistical Analysis of Inverse Weibull based on Step-Stress Partially Accelerated Life Tests with Unified Hybrid Censoring Data
Accelerated life testing (ALT) is a primary method for rapidly evaluating product reliability. This paper focuses on statistical inference for the invertedWeibull distribution under a step-stress partially ALT (SSPALT) model with a unified hybrid censoring scheme. This censoring scheme enhances the efficiency of statistical analysis and reduces overall test time. The inverted Weibull distribution is […]
Predicting the Trends of the Egyptian Stock Market Using Machine Learning and Deep Learning Methods
The prediction of stock price movements has remained a significant area of interest for researchers and investors, driven by the dynamic nature of financial markets and persistent economic fluctuations. The ability to forecast price trends enables investors to optimize their portfolios by identifying stocks likely to appreciate in value while avoiding those predicted to decline, […]