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Topics in compositional, seasonal and spatial-temporal time series

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TitleInfo
Title
Topics in compositional, seasonal and spatial-temporal time series
Name (type = personal)
NamePart (type = family)
Chang
NamePart (type = given)
Kun
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Kun Chang
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author
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Chen
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Rong
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Rong Chen
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Advisory Committee
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chair
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Xie
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Minge
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Minge Xie
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Advisory Committee
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internal member
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Xiao
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Han
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Han Xiao
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Advisory Committee
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internal member
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Cheng
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Jerry
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Jerry Cheng
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Advisory Committee
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
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2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
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eng
Abstract (type = abstract)
This dissertation studies several topics in time series modeling. The discussion on seasonal time series, compositional time series and spatial-temporal time series brings new insight to the existing methods. Innovative methodologies are developed for modeling and forecasting purposes. These topics are not isolated but to naturally support each other under rigorous discussions. A variety of real examples are presented from economics, social science and geoscience areas. The second chapter introduces a new class of seasonal time series models, treating the seasonality as a stable composition through time. With the objective of forecasting the sum of the next $ell$ observations, the concept of rolling season is adopted and a structure of rolling conditional distribution is formulated under the compositional time series framework. The probabilistic properties, the estimation and prediction, and the forecasting performance of the model are studied and demonstrated with simulation and real examples. The third chapter focuses on the discussion of compositional time series theories in multivariate models. It provides an idea to the modeling procedure of the multivariate time series that has sum constraints at each time. It also proposes a joint MLE method for threshold vector-error correction models. This chapter interprets the methodologies with an real example of the U.S. household consumption expenditures data. Threshold cointegration effects are analyzed on the U.S. real GDP growth rate. The estimation of TVECM is compared by the current two-step estimation method and the proposed joint MLE approach. Sensor allocation problem is studied in Chapter 4 under spatial-temporal models in Gaussian random fields. Given observations from existing sensors, the problem is solved by minimizing the integrated conditional variance based on different forecasting purposes. In this chapter, the time series are measured at different locations with both spatial and time series correlations. The spatial-temporal covariance structure is extensively discussed under both separable and nonseparable cases. The model is finally applied to the ozone level measurements in Harris County, Texas.
Subject (authority = RUETD)
Topic
Statistics and Biostatistics
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_6711
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ix, 102 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Time-series analysis
Subject (authority = ETD-LCSH)
Topic
Prediction theory
Note (type = statement of responsibility)
by Kun Chang
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3833V03
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Chang
GivenName
Kun
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-09-07 11:11:29
AssociatedEntity
Name
Kun Chang
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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