Statistical Models with High-Dimensional Predictors

具有高维预测变量的统计模型

• 批准号: 8605258
• 负责人: TODD OGDEN
• 金额: $ 16.3万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-07-19 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8605258
• 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涉及发展统计方法，将适用于许多非常高的