Applied Probability, Statistics and Operational Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1269

Special Issue Editors


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Guest Editor
Shanghai Center for Mathematical Sciences, Fudan University, 2005 Songhu Rd., Shanghai 200433, China
Interests: the interface of applied probability and statistics, especially the modeling of heavy-tailed phenomena in complex networks
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
Interests: Bayesian asymptotics; Bayesian nonparametrics; big data analysis; geometry and statistics; network analysis; probabilistic graphical models; topological data analysis

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Guest Editor
Department of Information Technology, Analytics, and Operations, Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA
Interests: financial risk management; portfolio optimization; insurance risk classification and pricing, and web search traffic forecasting

Special Issue Information

Dear Colleagues,

In the era of big data, a significant amount of research efforts has been devoted to the inter-discipline of applied probability, statistics, and operations research, with new methodologies and theories developed to resolve real-life problems. The focus of this Special Issue is oriented toward, but not limited to, the asymptotic analysis and optimization of probabilistic models, the development of novel statistical inference methods, as well as the data-driven decision-making process. Applications of new analytical tools to interesting real-life scenarios are also welcome. This Special Issue aims to provide a platform for researchers in the three disciplines to share their recent advances related to the fields and discuss insights into future research opportunities and challenges.

Dr. Tiandong Wang
Dr. Lizhen Lin
Dr. Zifeng Zhao
Guest Editors

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Keywords

  • applied probability
  • statistical inference
  • stochastic processes and applications
  • stochastic control
  • optimization
  • statistical learning
  • fuzzy methods

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Published Papers (1 paper)

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Research

22 pages, 1975 KiB  
Article
A Majority Theorem for the Uncapacitated p = 2 Median Problem and Local Spatial Autocorrelation
by Daniel A. Griffith, Yongwan Chun and Hyun Kim
Mathematics 2025, 13(2), 249; https://doi.org/10.3390/math13020249 - 13 Jan 2025
Cited by 1 | Viewed by 684
Abstract
The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic [...] Read more.
The existing quantitative geography literature contains a dearth of articles that span spatial autocorrelation (SA), a fundamental property of georeferenced data, and spatial optimization, a popular form of geographic analysis. The well-known location–allocation problem illustrates this state of affairs, although its empirical geographic distribution of demand virtually always exhibits positive SA. This latent redundant attribute information alludes to other tools that may well help to solve such spatial optimization problems in an improved, if not better than, heuristic way. Within a proof-of-concept perspective, this paper articulates connections between extensions of the renowned Majority Theorem of the minisum problem and especially the local indices of SA (LISA). The relationship articulation outlined here extends to the p = 2 setting linkages already established for the p = 1 spatial median problem. In addition, this paper presents the foundation for a novel extremely efficient p = 2 algorithm whose formulation demonstratively exploits spatial autocorrelation. Full article
(This article belongs to the Special Issue Applied Probability, Statistics and Operational Research)
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