Modeling and computations of multivariate datasets in space and time

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dc.contributor.author Demel, Samuel Seth
dc.date.accessioned 2013-04-24T19:28:23Z
dc.date.available 2013-04-24T19:28:23Z
dc.date.issued 2013-04-24
dc.identifier.uri http://hdl.handle.net/2097/15578
dc.description.abstract Spatio-temporal and/or multivariate dependence naturally occur in datasets obtained in various disciplines; such as atmospheric sciences, meteorology, engineering and agriculture. There is a great deal of need to effectively model the complex dependence and correlated structure exhibited in these datasets. For this purpose, this dissertation studies methods and application of the spatio-temporal modeling and multivariate computation. First, a collection of spatio-temporal functions is proposed to model spatio-temporal processes which are continuous in space and discrete over time. Theoretically, we derived the necessary and sufficient conditions to ensure the model validity. On the other hand, the possibility of taking the advantage of well-established time series and spatial statistics tools makes it relatively easy to identify and fit the proposed model in practice. The spatio-temporal models with some ARMA discrete temporal margin are fitted to Kansas precipitation and Irish wind datasets for estimation or prediction, and compared with some general existing parametric models in terms of likelihood and mean squared prediction error. Second, to deal with the immense computational burden of statistical inference for multi- ple attributes recorded at a large number of locations, we develop Wendland-type compactly supported covariance matrix function models and propose multivariate covariance tapering technique with those functions for computation reduction. Simulation studies and US tem- perature data are used to illustrate applications of the proposed multivariate tapering and computational gain in spatial cokriging. Finally, to study the impact of weather change on corn yield in Kansas, we develop a spatial functional linear regression model accounting for the fact that weather data were recorded daily or hourly as opposed to the yearly crop yield data and the underlying spatial autocorrelation. The parameter function is estimated under the functional data analysis framework and its characteristics are investigated to show the influential factor and critical period of weather change dictating crop yield during the growing season. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Spatio-temporal covariance modeling en_US
dc.subject Multivariate tapering en_US
dc.subject Spatial functional linear model en_US
dc.title Modeling and computations of multivariate datasets in space and time en_US
dc.type Dissertation en_US
dc.description.degree Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Juan Du en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2013 en_US
dc.date.graduationmonth May en_US


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