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Abstract Details
Activity Number:
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354
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #304391 |
Title:
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Statistical Analysis of Weather Data
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Author(s):
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Morteza Marzjarani*+ and Greg McNish
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Companies:
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Saginaw Valley State University and Saginaw Valley State University
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Address:
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SE-179 , University Center, MI, , USA
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Keywords:
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Bayesian ;
Simulation ;
Modeling
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Abstract:
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The research about the weather (temperature, precipitation, etc.) leaves numerous unanswered questions yet a wealth of information is available to us due to the advances in technology. Whether on earth or in space, there is a need to have knowledge of the weather around us. NASA satellites are capable of collecting massive data sets about the planets. The application of these data collected from a distance promises a number of surprising results. Weather data are inherently noisy. That is, they have to be cleaned. Outliers need to be addressed and handled in some ways. The next step would be to develop a statistical model which would be appropriate for the data set(s). Standard multivariate methods are difficult to apply to these data sets. In this research we will apply functional data methods such as Bayesian or hierarchical Bayesian modeling to analyze these data sets and provide interpretation for each application.
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