The DUS-transformed generalized linear failure rate distribution: Properties, estimation, and applications

Abstract

Probability distributions are fundamental tools in statistics, enabling the modeling and analysis of random phenomena across diverse fields such as engineering, medicine, finance, and environmental science. However, classical distributions often exhibit limitations in capturing the complexity of real-world data. This study addresses these limitations by introducing the DUS-transformed generalized linear failure rate (DUS-GLF) distribution, a novel extension of the generalized linear failure rate (GLF) distribution using the DUS transformation technique. The DUS-GLF distribution enhances the flexibility of the GLF model, enabling it to accommodate a wider range of data behaviors. Key statistical properties of the DUS-GLF distribution, such as its hazard rate function, moments, incomplete moments, entropy, and extropy, are derived. Sixteen estimation methods are employed to estimate the parameters of the DUS-GLF distribution, ensuring its practical applicability. The performance of the DUS-GLF distribution is evaluated using two real-world datasets, demonstrating its superior goodness-of-fit and predictive accuracy compared to other competing models. This research provides a robust statistical tool for complex data modeling, enhancing both theoretical and applied statistical analysis.