Spike and Slab Models: Theory and Applications

尖峰模型和板模型：理论与应用

• 批准号: 0705037
• 负责人: Hemant Ishwaran
• 金额: --
• 依托单位: Cleveland Clinic Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705037&HistoricalAwards=false
• 关键词: Spike; Slab; Models; Theory; Applications

The investigator seeks to expand the theory and application for rescaled spike and slab models, a class of Bayesian models, to address the general problem of variable selection and prediction. This will be accomplished in three distinct aims: (1) By developing theory as well as fast computational algorithms for non-orthogonal designs making using of spike and slab orthogonalization. The resulting predictor, a bagged ensemble derived using generalized ridge regression, will be shown to possess state of the art predictiveness, when one factors in interpretation over black-box prediction. Theory, in the form of finite sample arguments, will show this is due to selective shrinkage, a property whereby only truly zero coefficients are shrunk towards zero; (2) By developing general methodology for hard thresholding estimated regression coefficients; (3) By extending the rescaled spike and slab framework to include non-linear models such as generalized linear models and non-proportional survival regression models with time dependent predictors. Intellectually, this research will enhance our understanding of model building and outcome prediction, especially in ill-determined settings when the sample size is on the order of, or dominated by, the number of predictors (variables). This type of setting is becoming all too common in scientific settings. Among applications considered will be colon cancer genomics, an important public health problem. Currently, colorectal cancer is the second leading cause of cancer mortality in the adult American population, accounting for 140,000 new cases annually and 60,000 deaths. Although widely used, it is known that the current classification scheme is highly imperfect in reflecting the actual underlying molecular determinants of colon cancer behavior. For instance, upwards of 20% of patients whose cancers metastasize to the liver are not given life saving adjuvant chemotherapy based on the current clinical staging system. Thus, there is an important need for the identification of a molecular signature that will identify tumors that metastasize. Another area of application will be long-term prediction models for predicting outcomes following coronary artery bypass surgery, a widely used surgical modality for patients with obstructive coronary artery disease. Current long-term prediction models have serious limitations which have hindered our understanding. Yet another application will be in understanding survival behavior of heart and lung transplant recipients and the role viruses play in potential dysfunction of the transplanted organs. Methodology will be complemented by development of software for fast computational solutions in high dimensional settings.

研究者寻求扩展理论和应用的重新缩放的钉和板模型，一类贝叶斯模型，以解决变量选择和预测的一般问题。这将实现在三个不同的目标：(1)通过发展理论以及快速计算算法的非正交设计，利用钉和板正交。由此产生的预测器，使用广义脊回归导出的套袋集合，将显示出具有最先进的预测能力，当一个因素在解释中超过黑箱预测。以有限样本论证形式的理论将表明，这是由于选择性收缩，一种只有真正为零的系数才会收缩到零的性质；(2)开发硬阈值估计回归系数的一般方法；(3)通过扩展重新标度的尖峰和平板框架，使其包括非线性模型，如广义线性模型和具有时间依赖预测因子的非比例生存回归模型。在智力上，这项研究将增强我们对模型构建和结果预测的理解，特别是在不确定的情况下，当样本量与预测者（变量）的数量相同或受其支配时。这种类型的设置在科学设置中变得太常见了。考虑的应用将包括结肠癌基因组学，这是一个重要的公共卫生问题。目前，结直肠癌是美国成年人癌症死亡的第二大原因，每年有14万新发病例和6万例死亡。虽然被广泛使用，但已知目前的分类方案在反映结肠癌行为的实际潜在分子决定因素方面非常不完善。例如，根据目前的临床分期系统，超过20%的癌症转移到肝脏的患者没有接受挽救生命的辅助化疗。因此，有一个重要的需要鉴定分子特征，将识别转移的肿瘤。另一个应用领域将是预测冠状动脉搭桥手术后预后的长期预测模型，这是一种广泛用于阻塞性冠状动脉疾病患者的手术方式。目前的长期预测模型有严重的局限性，阻碍了我们的理解。另一个应用将是了解心脏和肺移植受者的生存行为以及病毒在移植器官潜在功能障碍中的作用。方法学将由高维环境下快速计算解决方案的软件开发来补充。