High-Dimensional Statistics and Network Data Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 20 June 2025 | Viewed by 1988
Special Issue Editors
Interests: statistical inference of networks; high-dimensional statistical inference; clustering; semiparametric inference; hypothesis testing; application of statistical methods in neuroscience, genomics, and astronomy
Interests: analysis of probabilistic models that arise from questions in biosciences; social sciences; physics and computer science; statistical inference problems that arise from questions in biosciences and other network data analysis
Special Issue Information
Dear Colleagues,
The data generation capacity of scientific studies has increased tremendously in recent years and continues to increase rapidly. With the increased complexity of data, multivariate data sets of large dimensions (also called high-dimensional data) and network data sets have come to prominence. Specifically, in the disciplines of genetics, neuroscience, finance, and computer science, to name a few, high-dimensional data have become commonplace. Complex networks and relational data sets are becoming common in various scientific fields, including social, economic, and biological science. In order to tackle high-dimensional and complex network data sets, significant efforts have been directed toward pathbreaking mathematical and statistical research. In terms of statistical methods, penalized and regularized estimators have become the standard to tackle estimation problems in high-dimensional data. New methods have been developed for inference in high-dimensional data thanks to innovations in multiple hypothesis testing, including false discovery rate control procedures. The likelihood and spectral methods have been developed to analyze network data sets. In order to build on the theoretical framework for the statistical methods of estimation and inference, significant efforts have been directed toward the study of concentration inequalities, large deviations, and random matrix theory in the disciplines of mathematical statistics and probability. We plan to focus on all these different directions of statistical research in high-dimensional and network data analysis in this Special Issue.
Dr. Sharmodeep Bhattacharyya
Dr. Shirshendu Chatterjee
Guest Editors
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Keywords
- high-dimensional data
- network data
- penalized estimation
- multiple hypothesis testing
- spectral methods
- change-point detection
- community detection
- conformal inference
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