Recursive Estimation of Time-Varying Parameters in Dynamic Factor Models for Nonstationary Psychological TIme Series

非平稳心理时间序列动态因子模型中时变参数的递归估计

• 批准号: 0852147
• 负责人: Peter Molenaar
• 金额: $ 25万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0852147&HistoricalAwards=false
• 关键词: Recursive; Estimation; Time; Varying; Parameters

Many psychological and biological processes have statistical characteristics which vary in time. Prime examples are adaptive, learning and developmental processes. Recently it has been shown mathematically that statistical analysis of processes with time-varying statistical characteristics has to be based on intensive repeated measurements of single subjects in order to obtain valid results. However, at present statistical techniques which would enable valid analyses of such processes are lacking. In this project innovative statistical modeling and estimation techniques will be developed which yield valid and reliable analyses of processes with a priori unknown time-varying characteristics. The new techniques can and will be applied to intensive repeated measurements of single subjects in real time, thus enabling high-fidelity tracking of the time-dependent fluctuations of key characteristics of psychological and biological processes as functions of momentary changes in environmental and subject-specific conditions. The new modeling and estimation techniques developed in this project will be validated in large scale computer simulation studies and implemented in generally accessible scientific software. They will be applied to a range of psychological and biological processes with time-varying statistical characteristics, including brain responses to transient stimuli measured by means of electroencephalographic and magnetic resonance imaging tools, and individual maturational, learning and developmental processes. A potential special field of application of the new modeling and estimation techniques developed in this project involves patient-specific continuous assessment and optimal treatment of disease processes such as diabetes type 1 and asthma. In sum, the outcomes of this project will for the first time enable valid and reliable statistical assessments and optimal guidance of psychological and biological processes with time-varying statistical characteristics.

许多心理和生物过程具有随时间变化的统计特征。最典型的例子是适应过程、学习过程和发展过程。近年来，数学上的研究表明，具有时变统计特征的过程的统计分析必须基于对单个对象的密集重复测量，才能获得有效的结果。但是，目前缺乏能够对这些过程进行有效分析的统计技术。在这个项目中，将开发创新的统计建模和估计技术，对具有先验未知时变特征的过程进行有效和可靠的分析。新技术可以并将应用于对单个对象的实时密集重复测量，从而能够高保真地跟踪心理和生物过程的关键特征随时间的波动，作为环境和特定对象条件下瞬间变化的功能。在这个项目中开发的新的建模和估计技术将在大规模计算机模拟研究中得到验证，并在普遍可访问的科学软件中实施。它们将被应用于一系列具有时变统计特征的心理和生物过程，包括通过脑电图和磁共振成像工具测量的大脑对短暂刺激的反应，以及个体的成熟、学习和发展过程。在这个项目中开发的新的建模和估计技术的一个潜在的特殊应用领域涉及对1型糖尿病和哮喘等疾病过程的特定患者的持续评估和最佳治疗。总而言之，该项目的成果将首次能够对具有时变统计特征的心理和生物过程进行有效和可靠的统计评估和最佳指导。