Year: 2025
Type: article
Abstract: High-dimensional statistics are used when n<5p, where n is the sample size and p is the number of predictors. Useful techniques include (a) the use of a sparse fitted model, (b) use of principal component analysis for dimension reduction, (c) use of alternative multivariate dispersion estimators instead of the sample covariance matrix, (d) eliminate weak predictors, and (e) stack low-dimensional estimators into a vector. Some variants and theory for these techniques will be given or reviewed.
Source: Stats
DOI: 10.3390/stats8030060
Author: David J. Olive
Institution: Southern Illinois University Carbondale
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4412393592