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An Efficient Two-Dimensional Functional Mixed-Effect Model Framework for Repeatedly Measured Functional Data
Cheng Cao1, Jiguo Cao2, Hao Pan3
1Department of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
Wearable accelerometers track daily physical activity, revealing complex patterns. A new two-dimensional functional mixed-effect model (2dFMM) shows significant links between activity and adolescent mental health, aiding intervention strategies.
Area of Science:
- Statistics
- Wearable Technology
- Public Health
Background:
- Wearable devices generate dense, serially correlated physical activity data over days.
- Existing models struggle to capture complex intraday and interday activity patterns.
- Understanding the link between physical activity and mental health is crucial for adolescents.
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