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Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data
2015
Annals of Applied Statistics
Ambulatory cardiovascular (CV) measurements provide valuable insights into individuals' health conditions in "real-life," everyday settings. Current methods of modeling ambulatory CV data do not consider the dynamic characteristics of the full data set and their relationships with covariates such as caffeine use and stress. We propose a stochastic differential equation (SDE) in the form of a dual nonlinear Ornstein--Uhlenbeck (OU) model with person-specific covariates to capture the morning
doi:10.1214/15-aoas846
pmid:26941885
pmcid:PMC4773035
fatcat:fzjcvi6ji5fyde4g2p5z574xt4