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Entropy
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Recent Advances in Statistical Inference for High Dimensional Data
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Recent Advances in Statistical Inference for High Dimensional Data

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 June 2024 | Viewed by 4198

Special Issue Editors

Prof. Dr. Junfeng Shang

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403-0206, USA
Interests: model selection in mixed models; generalized linear models; bootstrap methods; high dimensional data; modeling diagnostics; multiple comparison procedures; Bayesian inference
Prof. Dr. Hanfeng Chen

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403-0206, USA
Interests: mixture models; high dimensional data; zero-inflated population; generalized linear models; transformed data analysis

Special Issue Information

Dear Colleagues,

Statistical and computational challenges are created for high-dimensional data where the number of variables is greater than the number of cases. To cope with the challenges, more and more statistical methodologies for high-dimensional data have been developed and extensively applied in a wide range of fields including biology, medical informatics, engineering, psychology, financial time series, and climate forecasting. In this Special Issue, we welcome research work on high-dimensional data. We strongly encourage interdisciplinary work with real data analysis.  

This Special Issue calls for papers in, but not limited to, the following areas:

  • Statistical modeling methods for high-dimensional data and applications (e.g., regression, mixed models, mixture models, generalized linear models);
  • Model selection for high-dimensional data and applications;
  • Information theory and applications (e.g., decision optimization, clustering, classification);
  • Dimensionality reduction methods and applications in different real datasets;
  • Variable selection based on feature screening for high-dimensional data (e.g., bioinformatics, medical informatics, psychology, economics);
  • Statistical learning methods for high-dimensional data and applications (e.g., Lasso, splines, trees, random forests, neural networks, clustering, classification);
  • Applications based on Bayesian inference for high-dimensional data;
  • Statistical computing for high-dimensional data.

Prof. Dr. Junfeng Shang
Prof. Dr. Hanfeng Chen
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. 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

  • statistical modeling
  • statistical inference
  • model selection
  • information theory
  • dimensionality reduction
  • feature screening
  • statistical learning
  • interdisciplinary applications
  • bioinformatics
  • Bayesian inference
  • statistical computing

Published Papers (3 papers)

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Research

33 pages, 1742 KiB  
Article
A Blockwise Bootstrap-Based Two-Sample Test for High-Dimensional Time Series
by Lin Yang
Entropy 2024, 26(3), 226; https://doi.org/10.3390/e26030226 - 01 Mar 2024
Viewed by 807
Abstract
We propose a two-sample testing procedure for high-dimensional time series. To obtain the asymptotic distribution of our ℓ∞-type test statistic under the null hypothesis, we establish high-dimensional central limit theorems (HCLTs) for an α-mixing sequence. Specifically, we derive two HCLTs [...] Read more.
We propose a two-sample testing procedure for high-dimensional time series. To obtain the asymptotic distribution of our ℓ∞-type test statistic under the null hypothesis, we establish high-dimensional central limit theorems (HCLTs) for an α-mixing sequence. Specifically, we derive two HCLTs for the maximum of a sum of high-dimensional α-mixing random vectors under the assumptions of bounded finite moments and exponential tails, respectively. The proposed HCLT for α-mixing sequence under bounded finite moments assumption is novel, and in comparison with existing results, we improve the convergence rate of the HCLT under the exponential tails assumption. To compute the critical value, we employ the blockwise bootstrap method. Importantly, our approach does not require the independence of the two samples, making it applicable for detecting change points in high-dimensional time series. Numerical results emphasize the effectiveness and advantages of our method. Full article
(This article belongs to the Special Issue Recent Advances in Statistical Inference for High Dimensional Data)
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15 pages, 325 KiB  
Article
Distance Correlation-Based Feature Selection in Random Forest
by Suthakaran Ratnasingam and Jose Muñoz-Lopez
Entropy 2023, 25(9), 1250; https://doi.org/10.3390/e25091250 - 23 Aug 2023
Cited by 4 | Viewed by 1249
Abstract
The Pearson correlation coefficient (ρ) is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. The distance correlation measures all types of dependencies between random vectors X and Y [...] Read more.
The Pearson correlation coefficient (ρ) is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. The distance correlation measures all types of dependencies between random vectors X and Y in arbitrary dimensions, not just the linear ones. In this paper, we propose a filter method that utilizes distance correlation as a criterion for feature selection in Random Forest regression. We conduct extensive simulation studies to evaluate its performance compared to existing methods under various data settings, in terms of the prediction mean squared error. The results show that our proposed method is competitive with existing methods and outperforms all other methods in high-dimensional (p≥300) nonlinearly related data sets. The applicability of the proposed method is also illustrated by two real data applications. Full article
(This article belongs to the Special Issue Recent Advances in Statistical Inference for High Dimensional Data)
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26 pages, 357 KiB  
Article
Feature Screening for High-Dimensional Variable Selection in Generalized Linear Models
by Jinzhu Jiang and Junfeng Shang
Entropy 2023, 25(6), 851; https://doi.org/10.3390/e25060851 - 26 May 2023
Viewed by 1316
Abstract
The two-stage feature screening method for linear models applies dimension reduction at first stage to screen out nuisance features and dramatically reduce the dimension to a moderate size; at the second stage, penalized methods such as LASSO and SCAD could be applied for [...] Read more.
The two-stage feature screening method for linear models applies dimension reduction at first stage to screen out nuisance features and dramatically reduce the dimension to a moderate size; at the second stage, penalized methods such as LASSO and SCAD could be applied for feature selection. A majority of subsequent works on the sure independent screening methods have focused mainly on the linear model. This motivates us to extend the independence screening method to generalized linear models, and particularly with binary response by using the point-biserial correlation. We develop a two-stage feature screening method called point-biserial sure independence screening (PB-SIS) for high-dimensional generalized linear models, aiming for high selection accuracy and low computational cost. We demonstrate that PB-SIS is a feature screening method with high efficiency. The PB-SIS method possesses the sure independence property under certain regularity conditions. A set of simulation studies are conducted and confirm the sure independence property and the accuracy and efficiency of PB-SIS. Finally we apply PB-SIS to one real data example to show its effectiveness. Full article
(This article belongs to the Special Issue Recent Advances in Statistical Inference for High Dimensional Data)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Author 1: Nelum Hapuhinna

Author 2: Yi-Ju Lee

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