Timezone: »
In many areas of neuroscience and biological data analysis, it is desired to reveal common patterns among a group of subjects. Such analyses play important roles e.g., in detecting functional brain networks from fMRI scans and in identifying brain regions which show increased activity in response to certain stimuli. Group level techniques usually assume that all subjects in the group behave according to a single statistical model, or that deviations from the common model have simple parametric forms. Therefore, complex subject-specific deviations from the common model severely impair the performance of such methods. In this paper, we propose nonparametric algorithms for estimating the common covariance matrix and the common density function of several variables in a heterogeneous group of subjects. Our estimates converge to the true model as the number of subjects tends to infinity, under very mild conditions. We illustrate the effectiveness of our methods through extensive simulations as well as on real-data from fMRI scans and from arterial blood pressure and photoplethysmogram measurements.
Author Information
Andrey Zhitnikov (Technion)
Rotem Mulayoff (Technion)
Tomer Michaeli (Technion)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Revealing Common Statistical Behaviors in Heterogeneous Populations »
Fri Jul 13th 09:50 -- 10:00 AM Room A6
More from the Same Authors
-
2020 Poster: Unique Properties of Flat Minima in Deep Networks »
Rotem Mulayoff · Tomer Michaeli -
2019 Poster: Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff »
Yochai Blau · Tomer Michaeli -
2019 Oral: Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff »
Yochai Blau · Tomer Michaeli