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, we provide some new perspectives on aspects of high-dimensional statistical theory, emphasizing a number of open problems.
The work was supported by a UK Engineering and Physical Sciences Research Fellowship (to HSB).
We are grateful to five anonymous referees and the Associate Editor for references and detailed constructive criticism.
"Some Perspectives on Inference in High Dimensions." Statist. Sci. 37 (1) 110 - 122, February 2022. https://doi.org/10.1214/21-STS824