Statistics, Probability and Uncertainty

Research under Statistics, Probability and Uncertainty advances methodologies for data inference and risk quantification across diverse application domains. Emphasis lies on Bayesian and frequentist frameworks, time‐series modeling, multivariate analysis, and the development of robust estimators under model misspecification. Contributions proposing novel uncertainty‐propagation techniques, bootstrap methods or copula‐based dependency models enhance analytical rigor. Case studies in finance, epidemiology or engineering that illustrate the practical deployment of advanced statistical tools are encouraged. Theoretical work establishing asymptotic properties or finite‐sample guarantees complements applied investigations on real‐world datasets.