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Mathematics
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Advances in Multivariate Statistics and Related Topics for the Fourth...
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Advances in Multivariate Statistics and Related Topics for the Fourth Industrial Revolution

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3618

Special Issue Editors

Dr. Roelof Coetzer

E-Mail Website
Guest Editor
Department of Statistics, North-West University, Potchefstroom 2520, South Africa
Interests: design and analysis of experiments; statistical modelling; data science
Dr. Shawn Liebenberg

E-Mail Website
Guest Editor
Department of Statistics, North-West University, Potchefstroom 2520, South Africa
Interests: non-parametric statistics; multivariate statistics; bootstrap

Special Issue Information

Dear Colleagues,

Due to the fourth industrial revolution and the Internet of Things (IoT), more data are generated every second across all sectors of industry. The abundance of available data yields more complex problems and has created the opportunity for the development of new methods in the statistical, mathematical, and computer sciences to solve those problems. Specifically, the extraction of knowledge and insight from multivariate and multidimensional data has become critically important and a key focus research area over the last few years. Therefore, this Special Issue is dedicated to advancements in multivariate statistical methods and related topics, which are of interest to researchers and data science practitioners alike.

Dr. Roelof Coetzer
Dr. Shawn Liebenberg
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

  • multivariate data analysis and modeling
  • estimation and descriptive modeling
  • prediction and classification
  • deep learning
  • active learning
  • surrogate models
  • latent variable models
  • change point analysis

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Published Papers (2 papers)

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Research

27 pages, 6999 KiB  
Article
Realized Stock-Market Volatility of the United States and the Presidential Approval Rating
by Rangan Gupta, Yuvana Jaichand, Christian Pierdzioch and Reneé van Eyden
Mathematics 2023, 11(13), 2964; https://doi.org/10.3390/math11132964 - 3 Jul 2023
Cited by 2 | Viewed by 1115
Abstract
Studying the question of whether macroeconomic predictors play a role in forecasting stock-market volatility has a long and significant tradition in the empirical finance literature. We went beyond the earlier literature in that we studied whether the presidential approval rating can be used [...] Read more.
Studying the question of whether macroeconomic predictors play a role in forecasting stock-market volatility has a long and significant tradition in the empirical finance literature. We went beyond the earlier literature in that we studied whether the presidential approval rating can be used as a single-variable substitute in place of standard macroeconomic predictors when forecasting stock-market volatility in the United States (US). Political-economy considerations imply that the presidential approval rating should reflect fluctuations in macroeconomic predictors and, hence, may absorb or even improve on the predictive value for stock-market volatility of the latter. We studied whether the presidential approval rating has predictive value out-of-sample for realized stock-market volatility and, if so, which types of investors benefit from using it. Full article
(This article belongs to the Special Issue Advances in Multivariate Statistics and Related Topics for the Fourth Industrial Revolution)
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16 pages, 763 KiB  
Article
Digital Triplet: A Sequential Methodology for Digital Twin Learning
by Xueru Zhang, Dennis K. J. Lin and Lin Wang
Mathematics 2023, 11(12), 2661; https://doi.org/10.3390/math11122661 - 11 Jun 2023
Cited by 3 | Viewed by 1967
Abstract
A digital twin is a simulator of a physical system, which is built upon a series of models and computer programs with real-time data (from sensors or devices). Digital twins are used in various industries, such as manufacturing, healthcare, and transportation, to understand [...] Read more.
A digital twin is a simulator of a physical system, which is built upon a series of models and computer programs with real-time data (from sensors or devices). Digital twins are used in various industries, such as manufacturing, healthcare, and transportation, to understand complex physical systems and make informed decisions. However, predictions and optimizations with digital twins can be time-consuming due to the high computational requirements and complexity of the underlying computer programs. This poses significant challenges in making well-informed and timely decisions using digital twins. This paper proposes a novel methodology, called the “digital triplet”, to facilitate real-time prediction and decision-making. A digital triplet is an efficient representation of a digital twin, constructed using statistical models and effective experimental designs. It offers two noteworthy advantages. Firstly, by leveraging modern statistical models, a digital triplet can effectively capture and represent the complexities of a digital twin, resulting in accurate predictions and reliable decision-making. Secondly, a digital triplet adopts a sequential design and modeling approach, allowing real-time updates in conjunction with its corresponding digital twin. We conduct comprehensive simulation studies to explore the application of various statistical models and designs in constructing a digital triplet. It is shown that Gaussian process regression coupled with sequential MaxPro designs exhibits superior performance compared to other modeling and design techniques in accurately constructing the digital triplet. Full article
(This article belongs to the Special Issue Advances in Multivariate Statistics and Related Topics for the Fourth Industrial Revolution)
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