Advances in High-Dimensional Statistics

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 1081

Special Issue Editor


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Guest Editor
Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
Interests: High-dimensional statistics Variable selection Model combination Nonparametric statistics Causal inference and optimization

Special Issue Information

Dear Colleagues,

High-dimensional data analysis has emerged as a critical area of research with applications in fields such as machine learning, bioinformatics, and finance. The complexity and scale of high-dimensional datasets pose unique challenges that demand innovative statistical methodologies and computational approaches. This call for paper contributions invites researchers, academics, and practitioners to share their latest findings, methodologies, and applications in the realm of high-dimensional statistics.

We welcome submissions that address a broad range of topics related to advances in high-dimensional statistics, including but not limited to the following:

  • Dimensionality reduction techniques;
  • Sparse and low-rank modeling;
  • High-dimensional inference;
  • High-dimensional variable selection;
  • Graphical models for high-dimensional data;
  • Bayesian methods for high-dimensional problems;
  • High-dimensional clustering and classification;
  • Nonparametric approaches for high-dimensional data;
  • Computational challenges and scalable algorithms;
  • Applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.).

Dr. Yuwen Gu
Guest Editor

Manuscript Submission Information

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Keywords

  • dimensionality reduction techniques
  • sparse and low-rank modeling
  • high-dimensional inference
  • high-dimensional variable selection
  • graphical models for high-dimensional data
  • Bayesian methods for high-dimensional problems
  • high-dimensional clustering and classification
  • nonparametric approaches for high-dimensional data
  • computational challenges and scalable algorithms
  • applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.)

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Published Papers (1 paper)

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Research

17 pages, 396 KiB  
Article
Recursive Estimation of the Expectile-Based Shortfall in Functional Ergodic Time Series
by Fatimah A. Almulhim, Mohammed B. Alamari, Mustapha Rachdi and Ali Laksaci
Mathematics 2024, 12(24), 3956; https://doi.org/10.3390/math12243956 - 16 Dec 2024
Viewed by 718
Abstract
This paper considers the Recursive Kernel Estimator (RKE) of the expectile-based conditional shortfall. The estimator is constructed under a functional structure based on the ergodicity assumption. More preciously, we assume that the input-variable is valued in a pseudo-metric space, output-variable is scalar and [...] Read more.
This paper considers the Recursive Kernel Estimator (RKE) of the expectile-based conditional shortfall. The estimator is constructed under a functional structure based on the ergodicity assumption. More preciously, we assume that the input-variable is valued in a pseudo-metric space, output-variable is scalar and both are sampled from ergodic functional time series data. We establish the complete convergence rate of the RKE-estimator of the considered functional shortfall model using standard assumptions. We point out that the ergodicity assumption constitutes a relevant alternative structure to the mixing time series dependency. Thus, the results of this paper allows to cover a large class of functional time series for which the mixing assumption is failed to check. Moreover, the obtained results is established in a general way, allowing to particularize this convergence rate for many special situations including the kernel method, the independence case and the multivariate case. Finally, a simulation study is carried out to illustrate the finite sample performance of the RKE-estimator. In order to examine the feasibility of the recursive estimator in practice we consider a real data example based on financial time series data. Full article
(This article belongs to the Special Issue Advances in High-Dimensional Statistics)
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