Limiting Spectral Distribution of Large-Dimensional Sample Covariance Matrices Generated by the Periodic Autoregressive Model | Zendy

Jin Zou | Zendy; Dong Han | Zendy

Open Access

Limiting Spectral Distribution of Large-Dimensional Sample Covariance Matrices Generated by the Periodic Autoregressive Model

Author(s) -

Jin Zou,

Dong Han

Publication year - 2021

Publication title -

journal of mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.252

H-Index - 13

eISSN - 2314-4785

pISSN - 2314-4629

DOI - 10.1155/2021/9526991

Subject(s) - mathematics , autoregressive model , maximum entropy spectral estimation , covariance function , covariance , principle of maximum entropy , eigenvalues and eigenvectors , limiting , star model , moment (physics) , autocovariance , spectral density estimation , histogram , mathematical analysis , statistics , time series , fourier transform , autoregressive integrated moving average , mechanical engineering , physics , classical mechanics , quantum mechanics , engineering , artificial intelligence , computer science , image (mathematics)

The explicit representation for the limiting spectral moments of sample covariance matrices generated by the periodic autoregressive model (PAR) is established. We propose to use the moment-constrained maximum entropy method to estimate the spectral density function. The experiments show that the maximum entropy spectral density function curve obtained based on the fourth-order limiting spectral moment can match histograms of the eigenvalues of the covariance matrices very well.

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