Bayesian Methods for Variable Selection in Generalized/Nonlinear Models

广义/非线性模型中变量选择的贝叶斯方法

• 批准号: 1007871
• 负责人: Marina Vannucci
• 金额: $ 20万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1007871&HistoricalAwards=false
• 关键词: Bayesian; Methods; Variable; Selection; Generalized

The current proposal builds upon the P.I.'s experitize in variable selection and summarizes her current and future directions in the development of Bayesian methodologies. In particular, the P.I. plans to consider extensions to generalized models and to models that allow arbitrary nonlinear associations of a set of variables to a response. Inferential strategies for the proposed models are more challenging than the typical linear settings addressed by the P.I. in previous work. In addition, nonparametric priors will be investigated with the purpose of sharpening the selection and relaxing distributional assumptions of the models, such as those on random effects and on the error terms, often questionable in real-data applications.Methodologies developed through this project carry potential for significant impact in statistics and in applied fields in which high-dimension/low-sample-size data arise. Applications to data arising from interdisciplinary collaborations will demonstrate the practical usefulness of the proposed methods. In particular, the P.I. plans to build upon her recent interest in brain imaging data by investigating applications and extensions of Bayesian methods for variable selection to generalized linear and mixed models currently used for the analysis of such data. The broader impacts of this proposal are in its educational and training objectives, in its efforts to disseminate results and in the collaborative nature of the proposed research. The P.I. maintains an updated webpage on her research activities where papers and accompanying software are posted in a timely manner.

目前的提案建立在P.I.总结了她在变量选择方面的经验，并总结了她在贝叶斯方法学发展中的当前和未来的方向。特别是，P.I.计划考虑扩展到广义模型和模型，允许任意非线性协会的一组变量的响应。所提出的模型的推理策略比PI解决的典型线性设置更具挑战性。在以前的工作。此外，非参数先验将进行调查，锐化选择和放松分布假设的模型，如随机效应和误差项，往往在实际数据的应用程序中有问题的目的。通过这个项目开发的方法进行统计和应用领域，其中高维/低样本量的数据出现重大影响的潜力。对跨学科合作产生的数据的应用将证明所提出的方法的实际有用性。特别是，P.I.计划建立在她最近的兴趣在脑成像数据的研究应用和扩展贝叶斯方法的变量选择广义线性和混合模型目前用于分析这些数据。这一建议的更广泛影响体现在其教育和培训目标、传播成果的努力以及拟议研究的协作性质。私家侦探维持一个关于其研究活动的最新网页，及时张贴论文和附带软件。