Advances in Statistical Analysis for Health, Finance, Industry and Digital Economy

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

Deadline for manuscript submissions: 1 July 2025 | Viewed by 3704

Special Issue Editor


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Guest Editor
School of Statistics, University of International Business and Economics, Beijing, China
Interests: data mining; health data analysis; digital economy

Special Issue Information

Dear Colleagues,

Emerging sectors such as health, finance, industry and digital economy increasingly rely on complex data to address challenges and drive innovation. As the volume and variety of data continue to grow exponentially, the need for sophisticated statistical methods and analytical tools becomes imperative. However, advanced analytics applications often lag behind theoretical developments, especially in interdisciplinary research and practical applications.

This Special Issue aims to bridge this gap by soliciting contributions demonstrating the integration of cutting-edge statistical methods and novel applications. We welcome submissions from researchers, practitioners and academics at the forefront of data analysis in these fields. This Special Issue will provide a platform for exchanging ideas and disseminating knowledge, leading to meaningful advances in health, finance, industry and digital economy.

Prof. Dr. Lei Qin
Guest Editor

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Keywords

  • application of data mining
  • machine learning and optimization
  • generative AI
  • artificial intelligence in healthcare
  • health economics
  • bioinformatics
  • digital driven diagnostics and prognostics
  • option pricing
  • market microstructure
  • cryptocurrency
  • blockchain
  • digital economy
  • data assets

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Published Papers (2 papers)

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Research

17 pages, 2553 KiB  
Article
Technology Empowers Finance: Boundaries and Risks
by Zheng Ji, Xiaoqi Zhang, Han Liang and Yang Lyu
Mathematics 2024, 12(21), 3394; https://doi.org/10.3390/math12213394 - 30 Oct 2024
Viewed by 654
Abstract
BigTech credit has enhanced financial inclusion, but it also poses concerns with its boundaries. This article uses theoretical frameworks and numerical simulations to examine the risks and inclusiveness of technology-empowered credit services for “long-tail” clients. This research discovered that the discrepancy between the [...] Read more.
BigTech credit has enhanced financial inclusion, but it also poses concerns with its boundaries. This article uses theoretical frameworks and numerical simulations to examine the risks and inclusiveness of technology-empowered credit services for “long-tail” clients. This research discovered that the discrepancy between the commercial boundaries of BigTech credit and the technical limitations of risk management poses a risk in BigTech credit. The expanding boundaries of BigTech’s credit business may mitigate the representativeness of the data, resulting in a systematic deviation of unclear characteristics from the training sample data, which reduces the risk-control model’s ability to identify long-tail customers and raises the risk of credit defaults. Further computer simulations validate these results and demonstrate that competition among various companies would expedite the market’s transition over the boundary in case of a capital shortage. Finally, this article proposes setting up a joint-stock social unified credit technology company with data assets as an investment to facilitate the healthy and orderly development of financial technology institutions. Full article
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30 pages, 2330 KiB  
Article
A New Framework, Measurement, and Determinants of the Digital Divide in China
by Yuanren Zhou, Menggen Chen, Xiaojie Liu and Yun Chen
Mathematics 2024, 12(14), 2171; https://doi.org/10.3390/math12142171 - 11 Jul 2024
Viewed by 1363
Abstract
The digital divide (DD) reflects the inequality of the digital economy, while existing research lacks a comprehensive framework for investigating the measurement of DD and its determinants. This study constructs a new framework with a five-dimensional comprehensive index system. City-level data are used [...] Read more.
The digital divide (DD) reflects the inequality of the digital economy, while existing research lacks a comprehensive framework for investigating the measurement of DD and its determinants. This study constructs a new framework with a five-dimensional comprehensive index system. City-level data are used to measure China’s DD index from 2010 to 2020 at the national, regional, and provincial levels. Furthermore, this study investigates the decomposition of DD at both regional and provincial levels and the determinants of DD from the perspectives of physical, human, and social capital. The key results are: (1) China’s DD has generally exhibited a fluctuating downward trend. While it remains high in the eastern and western regions, it has shown a decline year by year. However, the DD within most provinces is on the rise. (2) The intra-regional and inter-provincial are the primary drivers of changes in national DD, with both intra-regional and intra-provincial contribution rates on the rise. (3) Economic growth, infrastructure, foreign trade, education, and online interaction significantly impact DD, and these determinants may change at different periods. This study intends to provide empirical support for bridging the DD, fostering the balanced development of the digital economy, and reducing social inequality. Full article
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