Submission Deadline: 15 November 2024 View: 66 Submit to Special Issue
We are pleased to announce a special issue titled "Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond." This special issue aims to explore the latest advancements in parametric survival analysis, focusing on the incorporation of trigonometric baseline distributions, machine learning techniques, and other innovative approaches that push the boundaries of traditional methodologies. The special issue emphasizes the applicability of parametric survival models in various fields, including engineering, economics, social sciences, medicine, education, and more.
Parametric survival models have played a crucial role in analyzing time-to-event data across diverse domains. To address the unique challenges posed by different fields, it is essential to explore new avenues and incorporate innovative techniques. This special issue aims to showcase the frontiers on parametric survival models by incorporating trigonometric baseline distributions. Trigonometric distributions, such as sine, cosine, and related functions, offer flexible and versatile alternatives, enabling more accurate modelling of periodic or cyclical behaviors observed in real-world survival data in engineering, economics, social sciences, medicine, education, and other fields.
Furthermore, this special issue highlights the integration of state-of-the-art machine learning techniques within parametric survival models. Machine learning algorithms provide powerful tools for feature selection, dimensionality reduction, and prediction, enhancing the performance and interpretability of parametric survival models across various domains. We welcome contributions that explore the synergies between machine learning and parametric survival analysis, pushing the boundaries of traditional methodologies in engineering, economics, social sciences, medicine, education, and other fields.
This special issue aligns with the scope of the journal by emphasizing computational statistical methods and their applications in modelling and simulation. Manuscripts submitted to this special issue should significantly contribute to modelling and simulation in general or utilize modelling and simulation in application areas, such as reliability analysis in engineering, duration analysis in economics, event history analysis in social sciences, survival analysis in medicine, time-to-event analysis in education, and other related fields. We encourage authors to explore innovative approaches, novel methodologies, and real-life applications that expand the frontiers of parametric survival models by incorporating trigonometric baseline distributions and machine learning techniques across multidisciplinary fields.
Submission guidelines and further details can be found on the journal's website [https://www.techscience.com/journal/CMES]. For any inquiries, please feel free to contact the guest editors directly. We eagerly await your groundbreaking contributions!
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