MARC보기
LDR01771nmm uu200385 4500
001000000332490
00520240805170903
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438153806
035 ▼a (MiAaPQ)AAI10787484
035 ▼a (MiAaPQ)umd:18861
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Law, Judith.
24510 ▼a Estimation of a Function of a Large Covariance Matrix Using Classical and Bayesian Methods.
260 ▼a [S.l.] : ▼b University of Maryland, College Park., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 84 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Partha Lahiri.
5021 ▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
520 ▼a In this dissertation, we consider the problem of estimating a high dimensional covariance matrix in the presence of small sample size. The proposed Bayesian solution is general and can be applied to different functions of the covariance matrix i
520 ▼a Using Monte Carlo simulations and real data analysis, we show that for small sample size, allocation estimates based on the sample covariance matrix can perform poorly in terms of the traditional measures used to evaluate an allocation for portf
590 ▼a School code: 0117.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a University of Maryland, College Park. ▼b Mathematics.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0117
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997398 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자