Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Function-on-scalar regression captures time-varying effects of physical activity interventions on daily trajectories better than FPCA followed by scalar regression, as shown in the STEP UP study.
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A new class of functional conditional autoregressive models
Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
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Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression
Function-on-scalar regression captures time-varying effects of physical activity interventions on daily trajectories better than FPCA followed by scalar regression, as shown in the STEP UP study.