A Composite Likelihood Approach to Binary Spatial Data
提出一种基于成对似然贡献的计算简单方法，用于空间上二元或指示数据的估计和预测，并通过马萨诸塞州舞毒蛾落叶的指示数据示例说明其应用。
Abstract Conventional geostatistics addresses the problem of estimation and prediction for continuous observations. But in many practical applications in public health, environmental remediation, or ecological research the most commonly available data are in the form of counts (e.g., number of cases) or indicator variables denoting above or below threshold values. Also, in many situations it is less expensive to obtain an imprecise categorical observation than to obtain precise measurements of the variable of interest (such as a contaminant). This article proposes a computationally simple method for estimation and prediction using binary or indicator data in space. The proposed method is based on pairwise likelihood contributions, and the large-sample properties of the estimators are obtained in a straightforward manner. We illustrate the methodology through application to indicator data related to gypsy moth defoliation in Massachusetts.