A1239
Title: Functional adaptive double-sparsity estimator for functional linear regression with multiple functional covariates
Authors: Xinyue Li - City University of Hong Kong (Hong Kong) [presenting]
Abstract: Wearable sensors have been increasingly used in health monitoring and early anomaly detection. Wearable devices can collect objective and continuous information on physical activity and vital signs and have great potential in studying the association with health outcomes. However, how to effectively analyze high-frequency multi-dimensional sensor data is challenging. A new Functional Adaptive Double-Sparsity Estimator (FadDoS) based on functional regularization of sparse group lasso with multiple functional predictors is proposed, which can achieve global sparsity via functional variable selection and local sparsity via zero-subinterval identification within coefficient functions. It is proved that the FadDoS estimator converges at a bounded rate and satisfies the oracle property under mild conditions. Extensive simulation studies confirm the theoretical properties and exhibit excellent performances compared to existing approaches. Application to a Kinect sensor study that utilized an advanced motion sensing device tracking human multiple joint movements and conducted among community-dwelling elderly demonstrates how FadDoS can effectively characterize the detailed association between joint movements and physical health assessments. The proposed method is not only effective in Kinect sensor analysis but also applicable to broader fields, where multi-dimensional sensor signals are collected simultaneously, to expand the use of sensor devices in health studies.
