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020 ▼a 9780438370968
035 ▼a (MiAaPQ)AAI10840918
035 ▼a (MiAaPQ)uchicago:14488
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 310
1001 ▼a Deb, Soudeep. ▼0 (orcid)0000-0003-0567-7339
24510 ▼a Irregular Spaced Data, Spatio-temporal Modeling and Clustering of Time Series.
260 ▼a [S.l.] : ▼b The University of Chicago., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 110 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Wei Biao Wu.
5021 ▼a Thesis (Ph.D.)--The University of Chicago, 2018.
520 ▼a In this thesis, three different problems in time series and random field have been discussed. First, for a general class of stationary random fields, we study the asymptotic properties of different parametric and nonparametric spectral density e
520 ▼a The second problem revolves around developing a spatio-temporal model with space-time interaction for air pollution data (PM2.5), which enables one to provide forecasts and insights about the air quality. The proposed model uses a parametric spa
520 ▼a The third problem in the thesis deals with a time series clustering problem. Using L2 distance between nonparametric spectral density estimates, a hierarchical clustering algorithm has been developed. Simulation studies show that the power of th
590 ▼a School code: 0330.
650 4 ▼a Statistics.
690 ▼a 0463
71020 ▼a The University of Chicago. ▼b Statistics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0330
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999759 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자