Asymptotics of hierarchical clustering for growing dimension
From MaRDI portal
Publication:392113
DOI10.1016/j.jmva.2013.11.010zbMath1278.62093OpenAlexW2100051950MaRDI QIDQ392113
Petro Borysov, James Stephen Marron, Jan Hannig
Publication date: 13 January 2014
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2013.11.010
Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items (6)
Joint estimation of precision matrices in heterogeneous populations ⋮ A high dimensional dissimilarity measure ⋮ Statistical Significance for Hierarchical Clustering ⋮ Asymptotic properties of hierarchical clustering in high-dimensional settings ⋮ A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets ⋮ Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings
Cites Work
- Unnamed Item
- Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
- Characteristic vectors of bordered matrices with infinite dimensions
- On the distribution of the roots of certain symmetric matrices
- PCA consistency in high dimension, low sample size context
- Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- Asymptotic behavior of M-estimators for the linear model
- Robust regression: Asymptotics, conjectures and Monte Carlo
- A General Framework For Consistency of Principal Component Analysis
- Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics
- PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
- Limit expressions for the risk of james‐stein estimators
- Consistency of Single Linkage for High-Density Clusters
- Asymptotic Statistics
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Weighted Distance Weighted Discrimination and Its Asymptotic Properties
- Geometric Representation of High Dimension, Low Sample Size Data
- Probability Inequalities for Sums of Bounded Random Variables
- The high-dimension, low-sample-size geometric representation holds under mild conditions
- DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
- Admissible clustering procedures
This page was built for publication: Asymptotics of hierarchical clustering for growing dimension
