Recent Developments in Statistical Research
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 358
Special Issue Editors
Interests: Bayesian statistics; biomedical statistics; statistical inference; expectation identity; Monte Carlo methods
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue “Recent Developments in Statistical Research”, which highlights transformative advances in modern statistical methodologies. This Special Issue aims to bridge cutting-edge theoretical innovations with practical applications across interdisciplinary domains. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: statistical learning, machine learning, Bayesian statistics, biomedical statistics, statistical inference, expectation identity, Monte Carlo methods, parametric statistics, nonparametric statistics, functional data analysis, time series, network models, econometrics, educational statistics, psychometrics, survival analysis, risk management, and artificial intelligence with applications. Recent advancements emphasize computational scalability, such as accelerated Markov chain Monte Carlo algorithms for real-time decision-making in precision medicine and climate science. Bayesian hierarchical models integrated with causal inference frameworks are revolutionizing evidence-based policy design, while probabilistic programming languages enhance reproducibility in genomics and environmental studies. Innovations in uncertainty quantification and adaptive neural networks are reshaping AI-driven predictive analytics. By fostering collaborations between statisticians and domain experts, this Special Issue seeks to address challenges in heterogeneous data environments and promote scalable, interpretable statistical systems for emerging scientific and societal needs. Submissions demonstrating novel methodological rigor or impactful interdisciplinary applications are encouraged.
We look forward to receiving your contributions.
Best wishes,
Dr. Yingying Zhang
Prof. Dr. Dong-Dong Pan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- statistical learning
- machine learning
- Bayesian statistics
- biomedical statistics
- statistical inference
- expectation identity
- Monte Carlo methods
- parametric statistics
- nonparametric statistics
- artificial intelligence with applications
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