High-Dimensional Linear Models: A Random Matrix Perspective

被引:0
|
作者
Jamshid Namdari
Debashis Paul
Lili Wang
机构
[1] University of California,Department of Statistics
[2] Zhejiang Gongshang University,School of Statistics and Mathematics
来源
Sankhya A | 2021年 / 83卷 / 2期
关键词
Multivariate statistics; linear models; random matrix theory.; Primary 62; Secondary 62H12; 62J05; 62J10;
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摘要
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.
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页码:645 / 695
页数:50
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