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Entropy
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Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition
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Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2097

Special Issue Editor

Prof. Dr. Yuehua Wu

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
Interests: statistical modeling and inference for data with a very complex structure and/or with high dimension
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Statistical models help us to understand the structure of systems or processes in various fields of engineering, natural sciences, and social sciences. One of the most important tasks in statistics is the development of methods and theories for building statistical models for datasets, which are approximations of the reality embodied in the observed data. In general, such models are not unique. For a given set of competing models, it is important to choose the best approximation model among them before performing statistical analysis.

Since data often exhibit complex structures, statistical models are expected to capture this complexity, which can further deepen our understanding of the underlying data-generating mechanisms and advance related fields in science and engineering. This Special Issue calls for newly developed statistical methods to model high-dimensional, complex data, especially methods based on entropy or information theory.

Prof. Dr. Yuehua Wu
Guest Editor

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. Entropy is an international peer-reviewed open access monthly 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

  • model selection
  • spatiotemporal modeling
  • cluster analysis
  • high-dimensional statistics
  • data mining
  • multiple change-point detection

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Related Special Issue

Published Papers (2 papers)

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Research

17 pages, 1232 KiB  
Article
Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis
by Kaida Cai, Wenzhi Fu, Zhengyan Wang, Xiaofang Yang, Hanwen Liu and Ziyang Ji
Entropy 2024, 26(9), 767; https://doi.org/10.3390/e26090767 - 7 Sep 2024
Viewed by 1082
Abstract
This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ [...] Read more.
This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics. Full article
(This article belongs to the Special Issue Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition)
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21 pages, 3074 KiB  
Article
Tail Risk Dynamics under Price-Limited Constraint: A Censored Autoregressive Conditional Fréchet Model
by Tao Xu, Lei Shu and Yu Chen
Entropy 2024, 26(7), 555; https://doi.org/10.3390/e26070555 - 28 Jun 2024
Viewed by 664
Abstract
This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate [...] Read more.
This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management. Full article
(This article belongs to the Special Issue Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition)
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