| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000332490 |
| 005 | | 20241202162907 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438153806 |
| 035 | |
▼a (MiAaPQ)AAI10787484 |
| 035 | |
▼a (MiAaPQ)umd:18861 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Law, Judith. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Mathematics. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997398
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |