Some perspectives on inference in high dimensions
File(s)
Accepted version
Author(s)
Battey, Heather
Cox, DR
Type
Journal Article
Abstract
With very large amounts of data, important aspects of statistical analysis may appear largely descriptive in that the role of probability sometimes seems limited or totally absent. The main emphasis of the present paper lies on contexts where formulation in terms of a probabilistic model is feasible and fruitful but to be at all realistic large numbers of unknown parameters need consideration. Then many of the standard approaches to statistical analysis, for instance direct application of the method of maximum likelihood, or the use of flat priors, often encounter difficulties. After a brief discussion of broad conceptual issues and the use of asymptotic analysis in statistical inference, we provide some new perspectives on aspects of high-dimensional statistical theory, emphasizing particularly a number of important open problems.
Date Issued
2022-02-01
Date Acceptance
2021-03-03
Citation
Statistical Science, 2022, 37, pp.110-122
ISSN
0883-4237
Publisher
Institute of Mathematical Statistics
Start Page
110
End Page
122
Journal / Book Title
Statistical Science
Volume
37
Issue
1
Copyright Statement
© 2022 Institute of Mathematical Statistics
Sponsor
Engineering and Physical Sciences Research Council
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/T01864X/1
EP/T01864X/1
Subjects
Statistics & Probability
0104 Statistics
Publication Status
Published