Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data

DOI10.1214/15-AOAS846zbMath1454.62358arXiv1511.05372OpenAlexW2213830168WikidataQ31052831 ScholiaQ31052831MaRDI QIDQ902935

Andrew Sherwood, Sy-Miin Chow, Hong-Tu Zhu, Zhao-hua Lu

Publication date: 4 January 2016

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.05372

zbMATH Keywords

Markov chain Monte Carlolatent processnonlinear processpopulation estimationirregularly spaced longitudinal datamultiresolution algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

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Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: an application to adolescent alcohol use with spatial covariates, Bayesian analysis of ambulatory blood pressure dynamics with application to irregularly spaced sparse data, Estimation of nonlinear mixed‐effects continuous‐time models using the continuous‐discrete extended Kalman filter, Improved bridge constructs for stochastic differential equations, Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching, Zero-inflated regime-switching stochastic differential equation models for highly unbalanced multivariate, multi-subject time-series data

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