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Activity Number:
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205
- Inference on Functional Data
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #321036
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Title:
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Robust Deep Neural Network Estimation for Multi-Dimensional Functional Data
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Author(s):
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Guanqun Cao* and Shuoyang Wang
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Companies:
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Auburn University and Auburn University
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Keywords:
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Functional data analysis;
Deep Neural networks;
M-estimators;
Rate of convergence;
ReLU activation function;
ADNI database
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Abstract:
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In this paper, we propose a robust estimator for the location function from multi-dimensional functional data. The proposed estimators are based on the deep neural networks with ReLU activation function. At the meanwhile, the estimators are less susceptible to outlying observations and model-misspecification. We provide the convergence rate of the proposed robust deep neural networks estimator in terms of the empirical norm. A simulation study and a real-world dataset illustrate the competitive performance of M-type deep neural networks in relation to the least-squares estimator on regular data and their superior performance on data that contain anomalies.
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Authors who are presenting talks have a * after their name.
