Generalized Method of Moments Approach for Spatial-Temporal Binary Data

• Activity Number: 182
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Kimberly Kaufeld*+
• Companies: University of Northern Colorado
• Keywords: Spatial-Temporal ; Generalized Linear Models ; Binary Data ; Generalized Method of Moments

Abstract

Binary data that are correlated across space and time often occur in health and ecological studies. The centered spatial-temporal autologistic regression model (Wang & Zheng, 2012) accounts for the spatial and temporal dependence that can occur in binary data. Statistical inference for the autologistic model has been based upon pseudo-likelihood, Monte Carlo Maximum Likelihood (MCML) or Bayesian hierarchical models. However, these methods require the full conditional distribution to be defined and with the complexity of spatial and temporal dependence and interactions between observations can cause convergence issues as well as an increase in computation time. In this research, we develop an alternative approach using generalized method of moments (GMM). In this method the full distribution does not need to be specified, but rather can be specified by the first two moments. A set of estimating equations with a specified working correlation structure is constructed to deal with the spatial and temporal dependence of the data. The GMM approach is demonstrated and results are compared to MCML using a real data example of bark beetle damage in the Rocky Mountain region.