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

许多心理和生物过程具有随时间变化的统计特征。主要的例子是适应、学习和发展过程。最近，它已被数学表明，统计分析的过程中随时间变化的统计特性，必须基于密集的重复测量的单个主题，以获得有效的结果。然而，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，创新的统计建模和估计技术将产生有效的和可靠的分析过程与先验未知的时变特性。这些新技术能够并将用于在真实的时间内对单个受试者进行密集的重复测量，从而能够高保真地跟踪作为环境和受试者特定条件瞬间变化的函数的心理和生物过程的关键特征的随时间变化的波动。本项目开发的新建模和估计技术将在大规模计算机模拟研究中得到验证，并在普遍可用的科学软件中实现。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瞬态刺激的反应，以及个人的成熟，学习和发展过程。本项目开发的新建模和估计技术的潜在特殊应用领域涉及特定于患者的连续评估和疾病过程（如1型糖尿病和哮喘）的最佳治疗。总之，这一项目的成果将首次使有效和可靠的统计评估和具有时变统计特征的心理和生物过程的最佳指导成为可能。

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.