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Activity Number: 195
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #310221
Title: Imputation of Missing Data Under a Spatial-Temporal Autologistic Regression Model
Author(s): Yanbing Zheng*+ and Zilong Wang
Companies: University of Kentucky and University of Kentucky
Keywords: autologistic model ; iteration KNN ; maximum entropy ; missing at random ; mountain pine beetle ; spatial-temporal process

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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