LDR | | 02019nmm uu200397 4500 |
001 | | 000000331279 |
005 | | 20240805164052 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438370968 |
035 | |
▼a (MiAaPQ)AAI10840918 |
035 | |
▼a (MiAaPQ)uchicago:14488 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 310 |
100 | 1 |
▼a Deb, Soudeep.
▼0 (orcid)0000-0003-0567-7339 |
245 | 10 |
▼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. |
502 | 1 |
▼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 |
710 | 20 |
▼a The University of Chicago.
▼b Statistics. |
773 | 0 |
▼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 |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999759
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |