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Article: Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model
Title | Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model |
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Authors | |
Issue Date | 2019 |
Publisher | Institute of Mathematical Statistics. The Journal's web site is located at http://www.imstat.org/aos/ |
Citation | The Annals of Statistics (Forthcoming) How to Cite? |
Abstract | This paper studies the joint limiting behavior of extreme eigen-values and trace of large sample covariance matrix in a generalized spiked population model, where the asymptotic regime is such that
the dimension and sample size grow proportionally. The form of the joint limiting distribution is applied to conduct Johnson-Graybill-type tests, a family of approaches testing for signals in a statistical model. For this, higher order correction is further made, helping alleviate the impact of finite-sample bias. The proof rests on determining the joint asymptotic behavior of two classes of spectral processes, corresponding to the extreme and linear spectral statistics respectively. |
Persistent Identifier | http://hdl.handle.net/10722/274047 |
ISSN | 2021 Impact Factor: 4.904 2020 SCImago Journal Rankings: 5.877 |
DC Field | Value | Language |
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dc.contributor.author | Li, Z | - |
dc.contributor.author | Han, F | - |
dc.contributor.author | Yao, JJ | - |
dc.date.accessioned | 2019-08-18T14:54:00Z | - |
dc.date.available | 2019-08-18T14:54:00Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | The Annals of Statistics (Forthcoming) | - |
dc.identifier.issn | 0090-5364 | - |
dc.identifier.uri | http://hdl.handle.net/10722/274047 | - |
dc.description.abstract | This paper studies the joint limiting behavior of extreme eigen-values and trace of large sample covariance matrix in a generalized spiked population model, where the asymptotic regime is such that the dimension and sample size grow proportionally. The form of the joint limiting distribution is applied to conduct Johnson-Graybill-type tests, a family of approaches testing for signals in a statistical model. For this, higher order correction is further made, helping alleviate the impact of finite-sample bias. The proof rests on determining the joint asymptotic behavior of two classes of spectral processes, corresponding to the extreme and linear spectral statistics respectively. | - |
dc.language | eng | - |
dc.publisher | Institute of Mathematical Statistics. The Journal's web site is located at http://www.imstat.org/aos/ | - |
dc.relation.ispartof | The Annals of Statistics | - |
dc.title | Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model | - |
dc.type | Article | - |
dc.identifier.email | Yao, JJ: jeffyao@hku.hk | - |
dc.identifier.authority | Yao, JJ=rp01473 | - |
dc.description.nature | preprint | - |
dc.identifier.hkuros | 302075 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0090-5364 | - |