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Mathematics
Special Issues
Significant Applications in Economics, Business, Management and Industrial Statistics
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Significant Applications in Economics, Business, Management and Industrial Statistics

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 2038

Special Issue Editors

Prof. Dr. Tzong-Ru Tsai

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Guest Editor
Department of Statistics, Tamkang University, Tamsui District, New Taipei City 251, Taiwan
Interests: reliability analysis; quality control; statistical modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianbao Chen

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
Interests: statistical theories and their applications; econometrics
Prof. Dr. Yuhlong Lio

E-Mail Website
Guest Editor
Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA
Interests: reliability analysis; quality control; kernel-smooth estimation; mathematical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational statistics and machine learning methodologies play an essential role in economics, business, management, and industry. Numerous researchers and technicians have dedicated their time to inventing novel computational statistics methodologies and machine learning algorithms to deal with data in various fields, such as engineering, reliability, economics, business, management, and surveys. The main purpose of this Special Issue of Mathematics is to provide a compendium of manuscripts that propose novel computational statistical methods for decision making, simulation studies, statistical inference, and relevant case studies. Topics of interest include, but are not limited to, the following:

  • Supply chain management and logistics.
  • Applications in economics, business, or management.
  • Bayesian methods and their applications.
  • Maintainability and availability.
  • Machine learning and its applications.
  • Modeling analysis and simulation.
  • Optimization and simulation.
  • Quality control and its applications.
  • Reliability modeling and life testing.
  • Risk assessment.

Prof. Dr. Tzong-Ru Tsai
Prof. Dr. Jianbao Chen
Prof. Dr. Yuhlong Lio
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • supply chain
  • logistics
  • Bayesian estimation
  • machine learning
  • reliability analysis
  • quality control
  • preventive maintenance
  • nowcasting
  • dynamic factor models

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

17 pages, 760 KiB  
Article
Parameter Estimation of Birnbaum-Saunders Distribution under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm
by Chanseok Park and Min Wang
Mathematics 2024, 12(11), 1757; https://doi.org/10.3390/math12111757 - 5 Jun 2024
Viewed by 613
Abstract
Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material’s fracture is dictated by its most severe defect. However, [...] Read more.
Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material’s fracture is dictated by its most severe defect. However, multimodal problems can become intricate due to potential censoring, a common constraint stemming from time and cost limitations during experiments. Additionally, determining the mode of failure can be challenging due to factors like the absence of suitable diagnostic tools, costly autopsy procedures, and other obstacles, collectively referred to as the masking problem. In this paper, we investigate the distribution of strength for multimodal failures with censored data. We consider both full and partial maskings and present an EM-type parameter estimate for the Birnbaum-Saunders distribution under competing risks. We compare the results with those obtained from other distributions, such as lognormal, Weibull, and Wald (inverse-Gaussian) distributions. The effectiveness of the proposed method is demonstrated through two illustrative examples, as well as an analysis of the sensitivity of parameter estimates to variations in starting values. Full article
(This article belongs to the Special Issue Significant Applications in Economics, Business, Management and Industrial Statistics)
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20 pages, 1182 KiB  
Article
Dynamic Event-Triggered Control for Delayed Nonlinear Markov Jump Systems under Randomly Occurring DoS Attack and Packet Loss
by Haiyang Zhang, Huizhen Chen, Lianglin Xiong and Yi Zhang
Mathematics 2024, 12(7), 1064; https://doi.org/10.3390/math12071064 - 1 Apr 2024
Cited by 3 | Viewed by 935
Abstract
This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the [...] Read more.
This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of the systems. Then, by constructing a general common Lyapunov functional and combining it with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through two examples. Full article
(This article belongs to the Special Issue Significant Applications in Economics, Business, Management and Industrial Statistics)
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