Abstract Details
Activity Number:
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85
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #311813
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View Presentation
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Title:
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Bayesian Analysis of Spatially Dependent Functional Responses with Spatially Dependent Multi-Dimensional Functional Predictors
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Author(s):
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Wen-Hsi Yang*+ and Christopher K. Wikle and Scott Holan and Brenton Myers and Kenneth A. Sudduth
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Companies:
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CSIRO Computational Informatics and University of Missouri and University of Missouri and University of Missouri and USDA/ARS
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Keywords:
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Basis functions ;
Diffuse reflectance spectroscopy ;
Matrix normal ;
Karhunen-Loeve ;
Penetrometer ;
Soil electrical conductivity
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Abstract:
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Modeling high-dimensional functional responses utilizing multi-dimensional functional covariates is complicated by spatial and/or temporal dependence in the observation in addition to high-dimensional predictors. To utilize such rich sources of information we develop multi-dimensional spatial functional models that employ low-rank basis function expansions to facilitate model implementation. These models are developed within a hierarchical Bayesian framework that accounts for several sources of uncertainty, including the error that arises from truncating the infinite-dimensional basis function expansions, as well as error in the observations and uncertainty in the parameters. We illustrate the predictive ability of such a model through a simulation study and an application that considers spatial models of soil electrical conductivity depth profiles using spatially dependent near infrared spectral images of electrical conductivity covariates.
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Authors who are presenting talks have a * after their name.
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