{"title":"样本协方差矩阵极值特征值偏差大","authors":"Denise Uwamariya, Xiangfeng Yang","doi":"10.1017/jpr.2022.130","DOIUrl":null,"url":null,"abstract":"\n\t <jats:p>Large deviations of the largest and smallest eigenvalues of <jats:inline-formula>\n\t <jats:alternatives>\n\t\t<jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mime-subtype=\"png\" xlink:href=\"S0021900222001309_inline1.png\" />\n\t\t<jats:tex-math>\n$\\mathbf{X}\\mathbf{X}^\\top/n$\n</jats:tex-math>\n\t </jats:alternatives>\n\t </jats:inline-formula> are studied in this note, where <jats:inline-formula>\n\t <jats:alternatives>\n\t\t<jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mime-subtype=\"png\" xlink:href=\"S0021900222001309_inline2.png\" />\n\t\t<jats:tex-math>\n$\\mathbf{X}_{p\\times n}$\n</jats:tex-math>\n\t </jats:alternatives>\n\t </jats:inline-formula> is a <jats:inline-formula>\n\t <jats:alternatives>\n\t\t<jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mime-subtype=\"png\" xlink:href=\"S0021900222001309_inline3.png\" />\n\t\t<jats:tex-math>\n$p\\times n$\n</jats:tex-math>\n\t </jats:alternatives>\n\t </jats:inline-formula> random matrix with independent and identically distributed (i.i.d.) sub-Gaussian entries. The assumption imposed on the dimension size <jats:italic>p</jats:italic> and the sample size <jats:italic>n</jats:italic> is <jats:inline-formula>\n\t <jats:alternatives>\n\t\t<jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mime-subtype=\"png\" xlink:href=\"S0021900222001309_inline4.png\" />\n\t\t<jats:tex-math>\n$p=p(n)\\rightarrow\\infty$\n</jats:tex-math>\n\t </jats:alternatives>\n\t </jats:inline-formula> with <jats:inline-formula>\n\t <jats:alternatives>\n\t\t<jats:inline-graphic xmlns:xlink=\"http://www.w3.org/1999/xlink\" mime-subtype=\"png\" xlink:href=\"S0021900222001309_inline5.png\" />\n\t\t<jats:tex-math>\n$p(n)={\\mathrm{o}}(n)$\n</jats:tex-math>\n\t </jats:alternatives>\n\t </jats:inline-formula>. This study generalizes one result obtained in [3].</jats:p>","PeriodicalId":50256,"journal":{"name":"Journal of Applied Probability","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large deviations of extremal eigenvalues of sample covariance matrices\",\"authors\":\"Denise Uwamariya, Xiangfeng Yang\",\"doi\":\"10.1017/jpr.2022.130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\t <jats:p>Large deviations of the largest and smallest eigenvalues of <jats:inline-formula>\\n\\t <jats:alternatives>\\n\\t\\t<jats:inline-graphic xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" mime-subtype=\\\"png\\\" xlink:href=\\\"S0021900222001309_inline1.png\\\" />\\n\\t\\t<jats:tex-math>\\n$\\\\mathbf{X}\\\\mathbf{X}^\\\\top/n$\\n</jats:tex-math>\\n\\t </jats:alternatives>\\n\\t </jats:inline-formula> are studied in this note, where <jats:inline-formula>\\n\\t <jats:alternatives>\\n\\t\\t<jats:inline-graphic xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" mime-subtype=\\\"png\\\" xlink:href=\\\"S0021900222001309_inline2.png\\\" />\\n\\t\\t<jats:tex-math>\\n$\\\\mathbf{X}_{p\\\\times n}$\\n</jats:tex-math>\\n\\t </jats:alternatives>\\n\\t </jats:inline-formula> is a <jats:inline-formula>\\n\\t <jats:alternatives>\\n\\t\\t<jats:inline-graphic xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" mime-subtype=\\\"png\\\" xlink:href=\\\"S0021900222001309_inline3.png\\\" />\\n\\t\\t<jats:tex-math>\\n$p\\\\times n$\\n</jats:tex-math>\\n\\t </jats:alternatives>\\n\\t </jats:inline-formula> random matrix with independent and identically distributed (i.i.d.) sub-Gaussian entries. The assumption imposed on the dimension size <jats:italic>p</jats:italic> and the sample size <jats:italic>n</jats:italic> is <jats:inline-formula>\\n\\t <jats:alternatives>\\n\\t\\t<jats:inline-graphic xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" mime-subtype=\\\"png\\\" xlink:href=\\\"S0021900222001309_inline4.png\\\" />\\n\\t\\t<jats:tex-math>\\n$p=p(n)\\\\rightarrow\\\\infty$\\n</jats:tex-math>\\n\\t </jats:alternatives>\\n\\t </jats:inline-formula> with <jats:inline-formula>\\n\\t <jats:alternatives>\\n\\t\\t<jats:inline-graphic xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\" mime-subtype=\\\"png\\\" xlink:href=\\\"S0021900222001309_inline5.png\\\" />\\n\\t\\t<jats:tex-math>\\n$p(n)={\\\\mathrm{o}}(n)$\\n</jats:tex-math>\\n\\t </jats:alternatives>\\n\\t </jats:inline-formula>. This study generalizes one result obtained in [3].</jats:p>\",\"PeriodicalId\":50256,\"journal\":{\"name\":\"Journal of Applied Probability\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Probability\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1017/jpr.2022.130\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Probability","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1017/jpr.2022.130","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Large deviations of extremal eigenvalues of sample covariance matrices
Large deviations of the largest and smallest eigenvalues of
$\mathbf{X}\mathbf{X}^\top/n$
are studied in this note, where
$\mathbf{X}_{p\times n}$
is a
$p\times n$
random matrix with independent and identically distributed (i.i.d.) sub-Gaussian entries. The assumption imposed on the dimension size p and the sample size n is
$p=p(n)\rightarrow\infty$
with
$p(n)={\mathrm{o}}(n)$
. This study generalizes one result obtained in [3].
期刊介绍:
Journal of Applied Probability is the oldest journal devoted to the publication of research in the field of applied probability. It is an international journal published by the Applied Probability Trust, and it serves as a companion publication to the Advances in Applied Probability. Its wide audience includes leading researchers across the entire spectrum of applied probability, including biosciences applications, operations research, telecommunications, computer science, engineering, epidemiology, financial mathematics, the physical and social sciences, and any field where stochastic modeling is used.
A submission to Applied Probability represents a submission that may, at the Editor-in-Chief’s discretion, appear in either the Journal of Applied Probability or the Advances in Applied Probability. Typically, shorter papers appear in the Journal, with longer contributions appearing in the Advances.