Inducement of population sparsity
File(s)
Published version
Author(s)
Battey, Heather
Type
Journal Article
Abstract
The pioneering work on parameter orthogonalization by Cox and Reid is presented as an inducement of abstract population-level sparsity. This is taken as a unifying theme for this article, in which sparsity-inducing parameterizations or data transformations are sought. Three recent examples are framed in this light: sparse parameterizations of covariance models, the construction of factorizable transformations for the elimination of nuisance parameters, and inference in high-dimensional regression. Strategies for the problem of exact or approximate sparsity inducement appear to be context-specific and may entail, for instance, solving one or more partial differential equations or specifying a parameterized path through transformation or parameterization space. Open problems are emphasized.
Date Issued
2023-09-01
Date Acceptance
2022-09-12
Citation
Canadian Journal of Statistics, 2023, 51 (3), pp.760-768
ISSN
0319-5724
Publisher
Wiley
Start Page
760
End Page
768
Journal / Book Title
Canadian Journal of Statistics
Volume
51
Issue
3
Copyright Statement
© 2023 The Author. The Canadian Journal of Statistics/La revue canadienne de statistique published by Wiley Periodicals LLC on behalf of Statistical Society of Canada / Société statistique du Canada.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://onlinelibrary.wiley.com/doi/full/10.1002/cjs.11751
Publication Status
Published
Date Publish Online
2023-01-09