POWRE: Methology for Spatial Models for Binary Data

POWRE：二进制数据空间模型方法

• 批准号: 9806243
• 负责人: Jennifer Hoeting
• 金额: $ 7.5万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-15 至 2001-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9806243&HistoricalAwards=false
• 关键词: POWRE; Methology; Spatial; Models; Binary

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.

9806243 Hoeting拟议的研究涉及二进制数据的空间模型，特别是autologistic模型的新方法的开发和应用。 在基本的自逻辑模型中，给定站点的响应是使用相邻站点响应的逻辑函数来预测的。 对于该模型的许多应用，还提供了其他信息，例如与目标响应相关的协变量。 当在感兴趣的区域上收集站点样本时，就是这种情况。 带有样本数据协变量的自逻辑模型（Hoeting et al，1997）允许用户将所有这些信息完全集成到模型中。 该研究将开发新的方法来解决与协变量的autologistic模型相关的四个研究问题，包括协变量的autologistic模型的变量选择方法，协变量的autologistic模型的贝叶斯模型平均（BMA），抽样检测概率的方法，以及协变量的autologistic模型的时空模型。 这些改进将使具有协变量的自动模型对更复杂的问题有用。