POWRE: Methology for Spatial Models for Binary Data

[9806243] hoting提出的研究涉及二元数据空间模型的新方法的开发和应用，特别是自定义模型。在基本的自适应模型中，利用邻近点响应的逻辑函数来预测给定点的响应。对于这个模型的许多应用来说，附加信息是可用的，比如与感兴趣的响应相关的协变量。这是在一个感兴趣的区域收集站点样本的情况。带有样本数据协变量的自定义模型（Hoeting et al ., 1997）允许用户将所有这些信息完全集成到模型中。提出的研究将开发新的方法来解决与协变量自动机模型相关的四个研究问题，包括协变量自动机模型的变量选择方法、协变量自动机模型的贝叶斯模型平均（BMA）方法、考虑抽样检测概率的方法以及协变量自动机模型的时空模型。这些改进将使带有协变量的自适应模型对更复杂的问题有用。

9806243 Hoeting The proposed research involves the development and application of new methodology for spatial models for binary data, particularly autologistic models. In the basic autologistic model, the response at a given site is predicted using a logistic function on the responses for the neighboring sites. For many applications of this model additional information is available, such as covariates that are related to the response of interest. This is the case when a sample of sites is collected over an area of interest. The autologistic model with covariates for sample data (Hoeting et al, 1997) allows the user to fully integrate all of this information into the model. The proposed research will develop new methodology to address four research problems related to the autologistic model with covariates including methodology for variable selection for the autologistic model with covariates, Bayesian model averaging (BMA) for the autologistic model with covariates, methodology to account for the probability of detection in sampling, and a space-time model for the autologistic model with covariates. Theses improvements will make the autologistic model with covariates useful for more complex problems.