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.
|Effective start/end date||8/1/09 → 7/31/12|
- National Science Foundation: $250,000.00