Some Useful Techniques for High-Dimensional Statistics

David J. Olive ()
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David J. Olive: School of Mathematical & Statistical Sciences, Southern Illinois University, Carbondale, IL 62901-4408, USA

Stats, 2025, vol. 8, issue 3, 1-15

Abstract: High-dimensional statistics are used when n < 5 p , 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.

Keywords: artificial intelligence; lasso; machine learning; model selection; outliers; PCA; PLS (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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