Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2013.06.017
Title: Estimation of the population spectral distribution from a large dimensional sample covariance matrix
Authors: Li, W.
Chen, J.
Qin, Y.
Bai, Z. 
Yao, J.
Keywords: Empirical spectral distribution
High-dimensional data analysis
Large sample covariance matrices
Marčenko-Pastur distribution
Stieltjes transform
Issue Date: Nov-2013
Citation: Li, W., Chen, J., Qin, Y., Bai, Z., Yao, J. (2013-11). Estimation of the population spectral distribution from a large dimensional sample covariance matrix. Journal of Statistical Planning and Inference 143 (11) : 1887-1897. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2013.06.017
Abstract: This paper introduces a new method to estimate the spectral distribution of a population covariance matrix from high-dimensional data. The method is founded on a meaningful generalization of the seminal Marčenko-Pastur equation, originally defined in the complex plane, to the real line. Beyond its easy implementation and the established asymptotic consistency, the new estimator outperforms two existing estimators from the literature in almost all the situations tested in a simulation experiment. An application to the analysis of the correlation matrix of S&P 500 daily stock returns is also given. © 2013 Elsevier B.V.
Source Title: Journal of Statistical Planning and Inference
URI: http://scholarbank.nus.edu.sg/handle/10635/105135
ISSN: 03783758
DOI: 10.1016/j.jspi.2013.06.017
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