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Activity Number:
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209
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #308782 |
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Title:
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Modeling Tilletia Indica Infection on Wheat Using Nonlinear Mixed Models with a Sharp Turning Point
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Author(s):
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Zhongwen Tang*+ and Shie-Shien Yang and Karen Garrett and I. Sharma and R. L. Bowden
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Companies:
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Kansas State University and Kansas State University and Kansas State University and Punjab Agriculture University and U.S. Department of Agriculture
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Address:
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101 Dickens Hall, Manhattan, KS, 66502,
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Keywords:
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Nonlinear mixed model ; Turning point ; Bayesian ; Maximum Likelihood ; SAS ; Winbugs
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Abstract:
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The infection and reproduction of Karnal bunt pathogen, Tilletia Indica, depend on the amount of sporidium encountering the host plants. A data driven nonlinear mixed model with a sharp turning point is built to precisely describe the relationship between the concentration of Tilletia Indica sporidium and the infection success percentage. Bayesian and Maximum likelihood methods are used to analyze the data. Model equations and turning points are estimated. SAS and WinBUGS are used for computation.
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