Statistical Inference and Analysis of High-Dimensional Data
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "D: Statistics and Operational Research".
Deadline for manuscript submissions: 15 February 2027 | Viewed by 505
Editor
Interests: minimum chi-square estimation; representative points; laplace distribution; dimension reduction; scrna-seq data
Special Issue Information
Dear Colleagues,
The exponential growth of modern datasets—featuring thousands of variables, complex dependencies, and often limited sample sizes—has fundamentally reshaped the landscape of statistical research. To address these challenges, we are pleased to announce a Special Issue of Mathematics dedicated to recent breakthroughs in the “Statistical Inference and Analysis of High-Dimensional Data”. This Special Issue aims to bridge theory and application, with a particular focus on three cutting-edge directions. We invite contributions advancing dimension reduction techniques in parametric and nonparametric statistical inference, including sparse modeling, sufficient dimension reduction, and penalized methods for complex dependency structures. We also seek works on linear and nonlinear dimension reduction for high-dimensional data visualization, from classical PCA to manifold learning, t-SNE, and UMAP, with an emphasis on interpretability and theoretical guarantees. Finally, we encourage submissions exploring high-dimensional techniques based on one-dimensional projection, such as projection pursuit, random projection, and the jackknife empirical likelihood for low-dimensional representations. Both methodological innovations and rigorous case studies are welcome. Join us to shape the future of inference in high dimensions.
Dr. Jiajuan Liang
Guest Editor
Manuscript Submission Information
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Keywords
- dimension reduction
- parametric/nonparametric inference
- high-dimensional visualization
- projection pursuit
- nonlinear manifolds
- random projection
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