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020 ▼a 9781085607780
035 ▼a (MiAaPQ)AAI13887000
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 510
1001 ▼a Patel, Pawan Prakash.
24514 ▼a The Circular Law for Random Matrices with Dependence.
260 ▼a [S.l.]: ▼b Indiana University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 130 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
500 ▼a Advisor: Connell, Christopher.
5021 ▼a Thesis (Ph.D.)--Indiana University, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a 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
590 ▼a School code: 0093.
650 4 ▼a Mathematics.
690 ▼a 0405
71020 ▼a Indiana University. ▼b Mathematics.
7730 ▼t Dissertations Abstracts International ▼g 81-02B.
773 ▼t Dissertation Abstract International
790 ▼a 0093
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491547 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK