Reprint

New Advances in Distribution Theory and Its Applications

Edited by
August 2025
288 pages
  • ISBN 978-3-7258-4983-3 (Hardback)
  • ISBN 978-3-7258-4984-0 (PDF)
https://doi.org/10.3390/books978-3-7258-4984-0 (registering)

Print copies available soon

This is a Reprint of the Special Issue New Advances in Distribution Theory and Its Applications that was published in

Computer Science & Mathematics
Summary

This Special Issue covers recent developments in distribution theory where the proliferation of 'new' distributions—often derived through standardized techniques—has led to the development of models that prioritize mathematical elegance and rigor over interpretability. While these models may exhibit increased complexity, they often fail to provide meaningful insights or flexibility beyond existing distributions in the literature.

The Special Issue aims to reinvigorate the field by highlighting contributions that emphasize both the flexibility of statistical models and the clarity of their parameter interpretations. It focuses on developing distribution models grounded in specific mechanisms or characteristics related to real-world contexts, as well as frameworks that advance families of distribution functions. The contributions herein introduce reparameterizations to enhance model interpretability, explore regression models using key characteristics or indicators, and offer original applications with real data.

Through this collection, the Special Issue seeks to foster deeper insights into statistical distribution theory, ensuring that new models remain practical and interpretable while advancing the flexibility and utility of existing frameworks.

Related Books

January 2025

Information-Theoretic Methods in Deep Learning

Computer Science & Mathematics
January 2020

Applied and Computational Statistics

Biology & Life Sciences
...
December 2021

Statistical Data Modeling and Machine Learning with Applications

Computer Science & Mathematics

The recommendations have been generated using an AI system.