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Everything about High-dimensional statistics
Study of high-dimensional data
In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than typically considered in classical multivariate analysis. The area arose owing to the emergence of many modern data sets in which the dimension of the data vectors may be comparable to, or even larger than, the sample size, so that justification for the use of traditional techniques, often based on asymptotic arguments with the dimension held fixed as the sample size increased, was lacking. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: High-dimensional statisticsA 5-minute orientation
- Skim a recent paper: Moving beyond sub-Gaussianity in high-dimensional statistics: applications in covariance estimation and linear regressionInfluential work since 2022
- Pick up a classic book: Pressure distributions from high Reynolds number tests of a Boeing BAC I airfoil in the Langley 0.3-Meter Transonic Cryogenic TunnelThe most-reprinted book on the subject
- Read the landmark paper: An introduction to recent advances in high/infinite dimensional statisticsCited 211 times
- Go sideways: Lasso (statistics)A closely related topic
- Go sideways: Functional data analysisA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
An introduction to recent advances in high/infinite dimensional statistics
High-Dimensional Statistics with a View Toward Applications in Biology
Recent advances in functional data analysis and high-dimensional statistics
Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
Compressible Distributions for High-Dimensional Statistics
Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications
Statistics of two-dimensional point vortices and high-energy vortex states
Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics
🆕 Influential since 2022
Moving beyond sub-Gaussianity in high-dimensional statistics: applications in covariance estimation and linear regression
Central limit theorem of linear spectral statistics of high-dimensional sample correlation matrices
Riemannian Statistics Meets Random Matrix Theory: Toward Learning From High-Dimensional Covariance Matrices
CLT for linear spectral statistics of high-dimensional sample covariance matrices in elliptical distributions
Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint
📚 Books worth reading
- 📕Pressure distributions from high Reynolds number tests of a Boeing BAC I airfoil in the Langley 0.3-Meter Transonic Cryogenic TunnelWilliam G. Johnson · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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