Models and Model Checking for Spatially-Varying Environmental Hazards and Decision Problems

有关公共卫生和公共支出的决策通常必须基于高度不确定的数据;例如，考虑到个人经济决策的测量质量以及公共计划和政策的影响。 在环境政策领域，知情决策所需的污染物测量等数据通常稀疏、空间分散，而且测量误差很大。 管理者和其他决策者对环境和其他公共决策问题中典型的巨大不确定性的一种反应是对接触、风险等使用“保守”（往往是夸大的）估计。然而，由于认识到政策应基于对可能的成本和效益的评估，近年来越来越多地使用效益-成本分析。 需要向前迈出的一个关键步骤，特别是对于空间变化的环境危害，是校准风险估计：这意味着在不同地区建议不同的行动方针和不同的数据收集战略。 为了有效地做到这一点，对相关风险敞口和风险进行空间建模是有用的。 该项目的目标是在具有不确定性（由于信息不完整）和真正的潜在可变性的环境中，为空间变化的危害开发更可靠的模型和模型检查方法。 近年来，在统计学领域中，使用贝叶斯方法对复杂数据结构进行建模已经取得了很大的进展。 需要取得更多进展的领域以及研究人员计划开展的工作包括模型拟合，计算，模型检查以及使用图形或地图显示推论。 研究人员计划特别关注模型检查和图形方法的使用，以建立对建模拟合结果的信心，以便个人和政策制定者拥有值得信赖的工具，使他们能够在决策时更好地考虑不确定性和可变性。 作为一个重要的例子，研究人员建议开发他们的模型的背景下，从家庭氡的风险补救，家庭氡数据从许多来源的综合分析的基础上。

Decisions concerning public health and public expenditures must often be based on highly uncertain data; for example, consider the uneven quality of measurements of individual economic decisions and effects of public programs and policies. In the field of environmental policy, data such as pollutant measurements that are required for informed decisions are usually sparse, spatially dispersed, and subject to substantial measurement error. One response by regulators and other policy makers to the large uncertainties typical of environmental and other public decision problems has been the use of `conservative` (often inflated) estimates of exposure, risk, etc. However, the recognition that policy should be based on assessment of both likely costs and benefits has led to increased use of benefit-cost analysis in recent years. A key step forward that needs to be made, especially for spatially-varying environmental hazards, is to calibrate risk estimates: this means recommending different courses of actions and also different data-gathering strategies in different areas. In order to do this effectively, it is useful to spatially model the relevant exposures and risks. The goal of this project is to develop more reliable methods of models and model-checking for spatially-varying hazards, in settings with uncertainty (due to incomplete information) and also true underlying variability. In recent years, much progress has been made in the field of statistics in modeling complex data structures using Bayesian methods. Areas in which more progress needs to be made and on which the investigators plan to work include model fitting, computation, model checking, and display of inferences using graphs or maps. The investigators plan to particularly focus on the use of model-checking and graphical methods to build confidence in the results of the modeling fitting, so that individuals and policy-makers will have trustworthy tools to allow them to take better account of uncertainty and variability when making decisions. As an important example, the investigators propose to develop their model in the context of remediation of risks from home radon, based on a combined analysis of home radon data from many sources.