Abstract:
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Brain-related datasets are High-dimensional datasets that have two main characteristics, sparsity and hierarchy. These types of data demand a novel methodology, specifically to capture pre-specified group structures and within-group correlation. The structural varying-coefficient regression (svReg) has been introduced to address these difficulties, however, it cannot detect nonlinear associations and is not robust to misspecification. We will present our most recent work that builds on svReg and that considers such additional challenges. We will present to what extent our proposed models are robust to misspecification and how well they identify nonlinear interactions.
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