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The Circular Law for Random Matrices with Dependence
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | The Circular Law for Random Matrices with Dependence. |
| 개인저자 | Patel, Pawan Prakash. |
| 단체저자명 | Indiana University. Mathematics. |
| 발행사항 | [S.l.]: Indiana University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 130 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085607780 |
| 학위논문주기 | Thesis (Ph.D.)--Indiana University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Connell, Christopher. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | In this dissertation, we consider the problem of determining the limiting spectral distribution for random matrices whose row distributions are permitted to have limited dependence and prove the Circular Law for Random Matrices with dependence. We assume mild moment conditions and give an extension of the Marcenko-Pastur theorem for this context. The main new features here are geometric conditions on the distributions which allow us to extend the circular law to this setting. We begin with a mild introduction, followed by chapters on general topics and methods of proof involving Universality Theorems in Random Matrix Theory. We continue with our proof of a more generalized Marcenko-Pastur theorem and results on the Least Singular Value of a Random Matrix. Finally, we end with an unrelated study into Covariance Estimators that highlights some connections between Random Matrix Theory and Portfolio Optimization |
| 일반주제명 | Mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
