Abstract Details
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Activity Number:
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195
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #310221 |
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Title:
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Imputation of Missing Data Under a Spatial-Temporal Autologistic Regression Model
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Author(s):
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Yanbing Zheng*+ and Zilong Wang
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Companies:
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University of Kentucky and University of Kentucky
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Keywords:
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autologistic model ;
iteration KNN ;
maximum entropy ;
missing at random ;
mountain pine beetle ;
spatial-temporal process
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Abstract:
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Missing data arise in the modern spatial-temporal data analysis, and the problems lie in incorrect measurements, faulty equipment, and manual data entry errors, etc. Here we consider the missing-at-random case for spatial-temporal binary data and impute missing values to estimate spatial and temporal effects in data analysis. We propose an iteration-KNN algorithm and a maximum entropy imputation algorithm for the imputation of missing data under a spatial-temporal autologistic regression model. The methodology is demonstrated via simulation studies and a real data example concerning mountain pine beetle outbreak in British Columbia, Canada.
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Authors who are presenting talks have a * after their name.
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