Statistical Models with High-Dimensional Predictors

具有高维预测变量的统计模型

• 批准号: 8917367
• 负责人: TODD OGDEN
• 金额: --
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8917367
• 关键词: Adopted; Algorithms; Biological; Biological Factors; Brain imaging; Complex; DNA Microarray Chip; Data; Data Analyses; Data Set; Depressed mood; Development; Diagnosis; Dimensions; Elements; Functional Magnetic Resonance Imaging; Future; Gene Expression; Goals; Heart; Image; Instruction; Light; Machine Learning; Mental Depression; Mental disorders; Methodology; Methods; Modeling; Neurobiology; Outcome; Patients; Performance; Positron-Emission Tomography; Procedures; Research; Risk; Severities; Simulate; Statistical Methods; Statistical Models; Structure; Suicide; Suicide attempt; Techniques; Training; Validation; base; contextual factors; data modeling; improved; indexing; insight; neglect; response; simulation; suicidal behavior; suicide attempter; tool; trait

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.

项目6涉及开发统计方法，该方法将适用于许多非常高的 作为孔戴中心的一部分收集的维数数据集。特别是，我们将专注于 具有单个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 数据以区分自杀式意见和抑郁症的非attepter或使用脑成像数据 预测患者对抑郁症治疗的反应。这种方法将采用新的强大 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。重点将是开发既可以实现的模型 准确的预测并提供稳定的可解释模型，从而更深入地了解 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 方法论，对现有数据集的应用以及将作为CONTE的一部分收集的方法 中心，以及使用仿真研究和实际数据的各种建模策略进行比较 验证。