Statistical Inference: Theory and Methods
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 37
Special Issue Editor
Special Issue Information
Dear Colleagues,
Statistical inference methods have undergone significant innovations in the last twenty years in various directions. The power of calculation tools has allowed the development of more complex models, often of high dimension, under which the classic concepts of mathematical statistics had to adapt. The discussion of inferential criteria, particularly in the area of divergences, enriched the choice of methods, which have also been widely used in machine learning (f-GAN, variational methods, etc.). The Bayesian paradigm also takes a prominent role in this context. Furthermore, taking into account massive data leads to rethinking classic questions, such as the properties of inference tools under misspecification; new standpoints for robustness concepts such as depth; or the loss of information by dimension reduction. We are therefore witnessing a significant renewal of the fundamental tools and concepts of our discipline; this volume proposes to expose some aspects of estimation issues in this perspective and will accept unpublished original papers and comprehensive reviews focused (but not restricted) on the following research areas:
- Variational inference;
- Conditional inference;
- Semi-parametric modeling and inference;
- Robustness in divergence-based inference;
- Divergence-based approaches for multivariate and dependent data;
- Inference for complex extreme values models;
- High-dimensional inference;
- Misspecification and robustness in a Bayesian framework;
- Robust methodologies for discrete and categorized data;
- Depth estimators, multivariate location, and scatter.
Prof. Dr. Michel Broniatowski
Guest Editor
Manuscript Submission Information
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Keywords
- variational inference
- conditional inference
- Bayesian inference
- divergence-based inference
- misspecification
- robustness
- machine learning
- outlier detection
- extreme values
- semi-parametric models
- depth estimators
- discrete and categorized data
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