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 491

Special Issue Editors


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Guest Editor
Department of Statistics and Data Science, Yunnan University, Kunming, China
Interests: Bayesian statistics; biomedical statistics; statistical inference; expectation identity; Monte Carlo methods

E-Mail Website
Guest Editor
Department of Statistics and Data Science, Yunnan University, Kunming, China
Interests: statistical genetics; biomedical statistics; applied statistics

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

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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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Published Papers (1 paper)

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Research

27 pages, 856 KiB  
Article
Equivalence Test and Sample Size Determination Based on Odds Ratio in an AB/BA Crossover Study with Binary Outcomes
by Shi-Fang Qiu, Xue-Qin Yu and Wai-Yin Poon
Axioms 2025, 14(8), 582; https://doi.org/10.3390/axioms14080582 - 27 Jul 2025
Viewed by 50
Abstract
Crossover trials are specifically designed to evaluate treatment effects within individual participants through within-subject comparisons. In a standard AB/BA crossover trial, participants are randomly allocated to one of two treatment sequences: either the AB sequence (where patients receive treatment A first and then [...] Read more.
Crossover trials are specifically designed to evaluate treatment effects within individual participants through within-subject comparisons. In a standard AB/BA crossover trial, participants are randomly allocated to one of two treatment sequences: either the AB sequence (where patients receive treatment A first and then cross over to treatment B after a washout period) or the BA sequence (where patients receive B first and then cross over to A after a washout period). Asymptotic and approximate unconditional test procedures, based on two Wald-type statistics, the likelihood ratio statistic, and the score test statistic for the odds ratio (OR), are developed to evaluate the equality of treatment effects in this trial design. Additionally, confidence intervals for OR are constructed, accompanied by an approximate sample size calculation methodology to control the interval width at a pre-specified precision. Empirical analyses demonstrate that asymptotic test procedures exhibit robust performance in moderate to large sample sizes, though they occasionally yield unsatisfactory type I error rates when the sample size is small. In such cases, approximate unconditional test procedures emerge as a rigorous alternative. All proposed confidence intervals achieve satisfactory coverage probabilities, and the approximate sample size estimation method demonstrates high accuracy, as evidenced by empirical coverage probabilities aligning closely with pre-specified confidence levels under estimated sample sizes. To validate practical utility, two real examples are used to illustrate the proposed methodologies. Full article
(This article belongs to the Special Issue Recent Developments in Statistical Research)
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