Abstract:
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Modern longitudinal data, for example from wearable devices, measures biological signals on a fixed set of participants at a diverging number of time points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analyzing the cumulatively growing dataset each time new data is collected. We propose a new estimation and inference framework for the streaming updating of point estimates and their standard errors across serially collected dependent datasets. We show how to recursively update estimates without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. Extensive simulations highlight the computational and statistical advantage of our approach over traditional statistical methods that analyze the cumulative longitudinal dataset. Finally, our streaming framework is used to investigate the relationship between physical activity and several diseases through the analysis of accelerometry data from the National Health and Nutrition Examination Survey.
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