Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health
arxiv(2023)
摘要
Mobile health has emerged as a major success for tracking individual health
status, due to the popularity and power of smartphones and wearable devices.
This has also brought great challenges in handling heterogeneous,
multi-resolution data which arise ubiquitously in mobile health due to
irregular multivariate measurements collected from individuals. In this paper,
we propose an individualized dynamic latent factor model for irregular
multi-resolution time series data to interpolate unsampled measurements of time
series with low resolution. One major advantage of the proposed method is the
capability to integrate multiple irregular time series and multiple subjects by
mapping the multi-resolution data to the latent space. In addition, the
proposed individualized dynamic latent factor model is applicable to capturing
heterogeneous longitudinal information through individualized dynamic latent
factors. Our theory provides a bound on the integrated interpolation error and
the convergence rate for B-spline approximation methods. Both the simulation
studies and the application to smartwatch data demonstrate the superior
performance of the proposed method compared to existing methods.
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