LDR | | 00000nmm u2200205 4500 |
001 | | 000000333703 |
005 | | 20250120102533 |
008 | | 181129s2018 ||| | | | eng d |
020 | |
▼a 9780438080195 |
035 | |
▼a (MiAaPQ)AAI10829918 |
035 | |
▼a (MiAaPQ)wisc:15428 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
049 | 1 |
▼f DP |
082 | 0 |
▼a 310 |
100 | 1 |
▼a Zhao, Zifeng. |
245 | 10 |
▼a Modeling Time Series via Copula and Extreme Value Theory. |
260 | |
▼a [S.l.] :
▼b The University of Wisconsin - Madison.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 144 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
500 | |
▼a Adviser: Zhengjun Zhang. |
502 | 1 |
▼a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018. |
520 | |
▼a Throughout my Ph.D. life, I mainly work on research topics about time series. This thesis consists of three representative works I have done for statistical modeling of time series. Modeling time series is a fundamental task in statistics, with |
520 | |
▼a For univariate time series, there is temporal dependence, where the past influences the future (autocorrelation). The modeling of univariate time series is a relatively well-studied research area, especially in financial applications. However, f |
520 | |
▼a For multivariate time series, there are both temporal dependence of each component univariate time series and cross-sectional dependence across all the component univariate time series. To accurately capture the behavior of multivariate time ser |
590 | |
▼a School code: 0262. |
650 | 4 |
▼a Statistics. |
690 | |
▼a 0463 |
710 | 20 |
▼a The University of Wisconsin - Madison.
▼b Statistics. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0262 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999362
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자
▼b 관리자 |