Abstract Number:
2643
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Heyang Ji (1), Lan Xue (2), Roger Zoh (1), Carmen Tekwe (1)
Institutions:
(1) Indiana University, Bloomington, IN, USA, (2) Oregon State University, Corvallis, OR, USA
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Wearable devices are often used to monitor physical activity behavior to study its influences on health outcomes. These devices are worn over multiple days to record activity patterns resulting in multi-level longitudinal high dimensional or functional data. And excess zeroes may be recorded for non-moving periods or due to missing data. In addition, some recent work has demonstrated that the accuracy of the devices in monitoring physical activity patterns depend on the intensity of the activities and wear time. While work on adjusting for biases due to measurement errors in functional data is a growing field, less work has been done to study missing data patterns, measurement errors and their combined influences on estimation in functional linear regression models. In this work, we propose semicontinuous modeling approaches to adjust for biases due to missing data, zero-inflation, and measurement errors in functional linear regression models. We demonstrate the finite sample properties of our proposed methods through simulations. These methods are applied to a school-based intervention study of physical activity on age and sex adjusted BMI among elementary school aged children.
Keywords:
Measurement error|Missing data|Zero-inflated functional covariate|Semicontinuous model|Physical activity data|
Sponsors:
Biometrics Section
Tracks:
High Dimensional Regression
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