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Regularized Modal Regression with Applications in Cognitive Impairment Prediction
Xiaoqian Wang · Hong Chen · Weidong Cai · Dinggang Shen · Heng Huang

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #154

Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort data.

Author Information

Xiaoqian Wang (University of Pittsburgh)
Hong Chen (University of Pittsburgh)
Weidong Cai (The University of Sydney)
Dinggang Shen (UNC-Chapel Hill)
Heng Huang (University of Pittsburgh)

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