Abstract:

Modern longitudinal data from wearable devices consist of biological signals at high-frequency time points and offer unparalleled opportunities for discovering new health insights. Distributed statistical methods have emerged as a powerful tool to overcome the computational burden of estimation and inference with these intensively measured outcomes, but methodology for distributed functional regression remains limited. We propose a distributed estimation and inference procedure that efficiently estimates both functional and scalar parameters for intensively measured longitudinal outcomes and overcomes computational difficulties through a scalable divide-and-conquer algorithm. We circumvent traditional basis selection problems by analyzing data in smaller subsets such that the basis functions have a low dimension. To address the challenges of combining estimates from dependent analyses, we propose a statistically efficient one-step estimator that avoids estimation of higher-order moments. We show theoretically and numerically that the proposed estimator is as statistically efficient as a non-distributed approach and more efficient computationally. We demonstrate the practicality of our approach through application of our method to accelerometer data from the NHANES data set.

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Topic: Colloquium -- Department of Statistics and Actuarial Science -- University of Iowa
Time: Apr 13, 2023 03:15 PM Central Time (US and Canada)

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Meeting ID: 919 6962 2615

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