Statistical Models with High-Dimensional Predictors

项目6涉及制定统计方法，将适用于许多非常高的国家， 作为Conte Center的一部分收集的多维数据集。特别是，我们将重点关注 具有单一结果变量和非常高维的预测因子的模型，例如，使用基因表达 数据来区分自杀者和抑郁的非自杀者，或者使用大脑成像数据 来预测病人对抑郁症治疗的反应该方法将采用强大的新 开发统计概念和工具，包括功能数据分析方法、机器学习 技术和预筛选算法。重点将放在开发既能实现 准确的预测，并提供稳定的可解释的模型，使人们能够更深入地了解 自杀行为和精神疾病的生物学基础。该项目涉及开发适当的 方法，应用于现有的数据集和那些将被收集作为合同的一部分， 中心，并使用模拟研究和真实的数据比较各种建模策略 验证。

Project 6 involves developing statistical methodology that will be applicable to many of the very high dimensional datasets that are being gathered as part of the Conte Center. In particular, we will focus on models with single outcome variables and very high-dimensional predictors, e.g., using gene expression data to discriminate between suicide attempters and depressed non attempters, or using brain imaging data to predict a patient's response to treatment for depression. This methodology will employ powerful newly developing statistical concepts and tools including functional data analytic methods, machine learning techniques, and prescreening algorithms. Emphasis will be on developing models that can both achieve accurate predictions and provide stable interpretable models, allowing for a deeper understanding of the biological basis of suicidal behavior and mental illness. The project involves development of appropriate methodology, application both to existing datasets and to those that will be gathered as part of the Conte Center, and comparison among the various modeling strategies using both simulation studies and real data validations.