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020 ▼a 9780438088733
035 ▼a (MiAaPQ)AAI10824969
035 ▼a (MiAaPQ)columbia:14736
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Wan, Phyllis.
24510 ▼a Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks.
260 ▼a [S.l.] : ▼b Columbia University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 174 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 ▼a Adviser: Richard A. Davis.
5021 ▼a Thesis (Ph.D.)--Columbia University, 2018.
520 ▼a This thesis covers four topics: i) Measuring dependence in time series through distance covariance
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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-
590 ▼a School code: 0054.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a Columbia University. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0054
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998718 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자