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Estimation of a Function of a Large Covariance Matrix Using Classical and Bayesian Methods
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Estimation of a Function of a Large Covariance Matrix Using Classical and Bayesian Methods. |
| 개인저자 | Law, Judith. |
| 단체저자명 | University of Maryland, College Park. Mathematics. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 84 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438153806 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Partha Lahiri. |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
