High-Dimensional Linear Models: A Random Matrix Perspective

Jamshid Namdari, Debashis Paul () and Lili Wang
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Jamshid Namdari: University of California
Debashis Paul: University of California
Lili Wang: Zhejiang Gongshang University

Sankhya A: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 7, 645-695

Abstract: Abstract Professor C.R.Rao’s Linear Statistical Inference is a classic that has motivated several generations of statisticians in their pursuit of theoretical research. This paper looks into some of the fundamental problems associated with linear models, but in a scenario where the dimensionality of the observations is comparable to the sample size. This perspective, largely driven by contemporary advancements in random matrix theory, brings new insights and results that can be helpful even for solving relatively low-dimensional problems. This overview also brings into focus the fundamental roles played by the eigenvalues of large covariance-type matrices in the theory of high-dimensional multivariate statistics.

Keywords: Multivariate statistics; linear models; random matrix theory.; Primary 62; Secondary 62H12; 62J05; 62J10 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13171-020-00219-y

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