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Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks. |
| 개인저자 | Wan, Phyllis. |
| 단체저자명 | Columbia University. Statistics. |
| 발행사항 | [S.l.]: Columbia University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 174 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438088733 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Richard A. Davis. |
| 요약 | This thesis covers four topics: i) Measuring dependence in time series through distance covariance |
| 요약 | Topic i) studies a dependence measure based on characteristic functions, called distance covariance, in time series settings. Distance covariance recently gathered popularity for its ability to detect nonlinear dependence. In particular, we char |
| 요약 | Topic ii) proposes a goodness-of-fit test for general classes of time series model by applying the auto-distance covariance function (ADCV) to the fitted residuals. Under the correct model assumption, the limit distribution for the ADCV of the r |
| 요약 | Topic iii) considers data in the multivariate regular varying setting where the radial part R is asymptotically independent of the angular part as thetaR goes to infinity. The goal is to estimate the limiting distribution of theta given R&rarr |
| 요약 | Topic iv) investigates inference questions related to the linear preferential attachment model for network data. Preferential attachment is an appealing mechanism based on the intuition "the rich get richer" and produces the well-observed power- |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
