Research Progress of Probability Statistics

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

Deadline for manuscript submissions: 28 September 2025 | Viewed by 1608

Special Issue Editor


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Guest Editor
School of Mathematics, Nanjing University, Nanjing 210093, China
Interests: quantum computing and quantum artificial intelligence; stochastic network; reflected diffusion approximation; stochastic (asymptotic) optimal control and (stochastic differential) game theory; stochastic (ordinary/partial) differential equations; machine learning and convolutional neural network

Special Issue Information

Dear Colleagues,

This Special Issue covers all aspects of probability and statistics and aims to showcase papers promoting the development of probability and statistics, ranging from their fundamental theory and methodology to their applications. Research papers featuring stochastics in all areas are welcome. This Issue also aims to highlight papers which handle the randomness in real-world systems through probability and statistical techniques. Furthermore, research papers integrating modern technology with stochastic analysis in (but not limited to) artificial intelligence, machine learning, convolutional neural network, communication system, data science, quantum computing, bioinformatics, operations research, finance, etc., are welcome.

Prof. Dr. Wanyang Dai
Guest Editor

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Keywords

  • probability
  • statistics
  • stochastics

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Published Papers (2 papers)

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Research

10 pages, 1196 KiB  
Article
A Study of Winning Percentage in the MLB Using Fuzzy Markov Regression
by Seung Hoe Choi and Seo-Kyung Ji
Mathematics 2025, 13(6), 1008; https://doi.org/10.3390/math13061008 - 20 Mar 2025
Viewed by 208
Abstract
In this study, we analyze the winning percentage of 16 teams that have participated in Major League Baseball since 1901. First, 69 variables for each team are classified into pitching, batting, and fielding using factor analysis, and then the effect of the newly [...] Read more.
In this study, we analyze the winning percentage of 16 teams that have participated in Major League Baseball since 1901. First, 69 variables for each team are classified into pitching, batting, and fielding using factor analysis, and then the effect of the newly classified variables on the winning percentage is analyzed. In addition, after expressing each team’s winning rate as a fuzzy number using a fuzzy partition, the linear relationship between the previous year and the next year using the fuzzy probability is investigated, and we estimate the fuzzy regression model and Markov regression model using the Double Least Absolute Deviation (DLAD) method. The proposed fuzzy model describes variables that affect the winning percentage of the next year according to the winning rate of the previous year. The estimated fuzzy regression model showed that the on-base percentage allowed by the pitcher and the on-base percentage of the batter had the greatest effect on the winning percentage. Full article
(This article belongs to the Special Issue Research Progress of Probability Statistics)
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11 pages, 273 KiB  
Article
Some New Bivariate Properties and Characterizations Under Archimedean Copula
by Qingyuan Guan, Peihua Jiang and Guangyu Liu
Mathematics 2024, 12(23), 3714; https://doi.org/10.3390/math12233714 - 26 Nov 2024
Viewed by 589
Abstract
This paper considers comparing properties and characterizations of the bivariate functions under Archimedean copula. It is shown that some results of the usual stochastic order for the bivariate functions in the independent case are generalized to the Archimedean copula-linked dependent case, and we [...] Read more.
This paper considers comparing properties and characterizations of the bivariate functions under Archimedean copula. It is shown that some results of the usual stochastic order for the bivariate functions in the independent case are generalized to the Archimedean copula-linked dependent case, and we also derive some characterizations of different bivariate functions composed by Archimedean copula-linked dependent random variables. These results generalize some existing results in the literature and bring conclusions closer to reality. Two applications in scheduling problems are also provided to illustrate the main results. Full article
(This article belongs to the Special Issue Research Progress of Probability Statistics)
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