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Abstract Details

Activity Number: 596
Type: Topic Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #305118
Title: Covariance Models for Vector Random Fields in Space and Time
Author(s): Juan Du*+ and Chunsheng Ma
Companies: Kansas State University and Wichita State University
Address: Department of Statistics, Manhattan, KS, 66506,
Keywords: Spatial statistics ; Covariance matrix function ; Vector random field ; Gaussian random field

Multivariate data over space and time are often observed in various disciplines, such as atmospheric sciences, meteorology, engineering and agriculture. The modeling and simulation study of this type of data call on the development of the vector random fields with various properties for both theoretical study and practical use. Several families of the covariance matrix structures through an efficient approach are proposed to model vector random fields in Gaussian and non-Gaussian cases, as well as stationary and non-stationary situations. Basic theoretical properties of the proposed multivariate covariance models are explored to provide some guidance on the practical application of these models. The model fitting and applications are demonstrated using simulation study and some weather data sets.

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