High dimensional correlation matrices: the central limit theorem and its applications
By Jiti Gao, Xiao Han, Guangming Pan, Yanrong Yang. 2017
76 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Discrete Mathematics and Combinatorics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/58f955c7ff330d7612b2b2b00190b77fd98df52b
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In Artificial Intelligence
In Discrete Mathematics and Combinatorics
In Statistics and Probability
Citations in the corpus
- High-dimensional consistent independence testing with maxima of rank correlations (2018, methodology): “Using covariance/correlation statistics such as Pearson’s r, Spearman’s ρ, and Kendall’s τ , Bao et al. (2012), Gao et al. (2017), Han et al. (2018), and Bao (2019) proposed revised versions of Roy’s largest root test.”
- Estimating Number of Factors by Adjusted Eigenvalues Thresholding (2019, background): “It has been shown (Guttman, 1954; Kaiser, 1960, 1961) that the number of common factors is less than or equal to the number of R ’s eigenvalues greater than 1.”