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Wiley Interdisciplinary Reviews:
WIREs Computational Statistics
Volume 12 Issue 2 (March 2020)
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Envelope methods
R. Dennis Cook
Published Online: Oct 03 2019
DOI: 10.1002/wics.1484
Envelopes reduce estimative and predictive variation relative to standard multivariate methodology by projecting out variation that is not material to the goals of the analysis.
Abstract Full article on Wiley Online Library:   HTML | PDF
Review of univariate and bivariate exponentiated exponential‐geometric distributions
Felix Famoye, Carl Lee
Published Online: Aug 02 2019
DOI: 10.1002/wics.1481
Abstract Full article on Wiley Online Library:   HTML | PDF

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High‐dimensional covariance matrix estimation
Clifford Lam
Published Online: Oct 23 2019
DOI: 10.1002/wics.1485
Covariance matrix of a set of cosmological data (log‐scale).
Abstract Full article on Wiley Online Library:   HTML | PDF
Tensor decomposition for dimension reduction
Yu‐Hsiang Cheng, Tzee‐Ming Huang, Su‐Yun Huang
Published Online: Jul 22 2019
DOI: 10.1002/wics.1482
PCA is the most popular unsupervised dimension reduction method for compressed data representation. Multilinear PCA is its extension to tensor‐structured data. Further extension to supervised tensor dimension reduction is also introduced.
Abstract Full article on Wiley Online Library:   HTML | PDF

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