LDR | | 02022nmm uu200421 4500 |
001 | | 000000333896 |
005 | | 20240805174736 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438291379 |
035 | |
▼a (MiAaPQ)AAI10826454 |
035 | |
▼a (MiAaPQ)ucdavis:17983 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 510 |
100 | 1 |
▼a Sonmez, Ozan. |
245 | 10 |
▼a Structural Breaks in Functional Time Series Data. |
260 | |
▼a [S.l.] :
▼b University of California, Davis.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 150 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
500 | |
▼a Adviser: Alexander Aue. |
502 | 1 |
▼a Thesis (Ph.D.)--University of California, Davis, 2018. |
520 | |
▼a Structural break analysis in functional data is explored. First, methodology is proposed to uncover structural breaks in the mean function of functional data that is "fully functional" in the sense that it does not rely on dimension reduction te |
520 | |
▼a Second, we establish the weak convergence of the process of partial sample estimates of the eigenvalues and eigenfunctions, or principal components, defined by the covariance operator of stationary functional time series. Based on the asymptotic |
520 | |
▼a Finally, we discuss an R package, fChange, for structural break analysis in functional data that implements the proposed methods. This package aims to provide practical implementations that can be used by interested practitioners. |
590 | |
▼a School code: 0029. |
650 | 4 |
▼a Mathematics. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0405 |
690 | |
▼a 0984 |
710 | 20 |
▼a University of California, Davis.
▼b Statistics. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0029 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998890
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |