A0657
Title: Statistical inference for streamed longitudinal data
Authors: Emily Hector - North Carolina State University (United States) [presenting]
Jingshen Wang - University of Michigan (United States)
Lan Luo - University of Iowa (United States)
Abstract: Modern longitudinal data, for example from wearable devices, measure 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. 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.
