Collaborative proposal: Variable Selection in the high dimensional, low sample size setting -- Beyond the Linear Regression and Normal Errors Model

革命性的新技术正在以十年前无法想象的分辨率产生高通量生物数据。这些新形式的数据为统计人员和计算机科学家带来了巨大的挑战和机遇。该项目开发了新的复杂的统计方法和计算算法，用于分析和整合复杂的高维数据。这项工作的动机是与Cornell-Ithaca和Weill Cornell医学院的领先生物科学家合作，他们在不同的研究领域工作，包括植物生物学，营养学，神经学，癌症表观基因组学和兽医学。该项目的目标是为高维、低样本量、高通量生物数据开发新的统计模型和计算算法，包括用于微阵列分析、数量性状基因座鉴定、关联作图、无标记鸟枪蛋白质组学和代谢组学的新方法。所提出的方法涉及现代统计构建块的创新扩展，包括使用随机效应进行正则化，收缩估计，贝叶斯统计以及后验分类和预测的混合。新的修改的期望最大化算法提出了可扩展的和有效的模型拟合和推理。

Revolutionary new technologies are producing high-throughput biological data at a resolution that was unthinkable only a decade ago. These new forms of data pose enormous challenges and opportunities for statisticians and computer scientists. This project develops new sophisticated statistical methods and computational algorithms for analyzing and integrating complex high-dimensional data. The work is motivated by collaborations with leading biological scientists at Cornell-Ithaca and Weill Cornell Medical College working in diverse research areas including plant biology, nutrition, neurology, cancer epigenomics, and veterinary medicine. The goal of this project is to develop new statistical models and computational algorithms for high-dimensional, low sample size, high-throughput biological data, including new methods for the analysis of microarrays, the identification of quantitative trait loci, association mapping, label-free shotgun proteomics and metabolomics. The proposed methods involve innovative extensions of modern statistical building blocks, including the use of random effects for regularization, shrinkage estimation, Bayesian statistics, and mixtures for posterior classification and prediction. Novel modifications of the expectation-maximization algorithm are proposed for scalable and efficient model fitting and inference.