Bayesian Nonparametric Longitudinal Data Analysis

Content Provider	Scilit
Author	Quintana, Fernando A. Johnson, Wesley O. Waetjen, L. Elaine Gold, Ellen B.
Copyright Year	2016
Description	Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
Related Links	http://europepmc.org/articles/pmc5373670?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373670/pdf
Ending Page	1181
Page Count	14
Starting Page	1168
ISSN	01621459
e-ISSN	1537274X
DOI	10.1080/01621459.2015.1076725
Journal	Journal of the American Statistical Association
Issue Number	515
Volume Number	111
Language	English
Publisher	Informa UK Limited
Publisher Date	2016-07-02
Access Restriction	Open
Subject Keyword	Journal: Journal of the American Statistical Association Statistics and Probability Bayesian Nonparametric Covariance Estimation Dirichlet Process Mixture Gaussian Process Mixed Model Ornstein-uhlenbeck Process Study of Women Across the Nation (swan)
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty