Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, With Application to Glaucoma Data

• Published in: Journal of the American Statistical Association Vol. 114; no. 526; pp. 495 - 513
• Main Authors: Lee, Wonyul, Miranda, Michelle F., Rausch, Philip, Baladandayuthapani, Veerabhadran, Fazio, Massimo, Downs, J. Crawford, Morris, Jeffrey S.
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 03.04.2019; Taylor & Francis Group, LLC; Taylor & Francis Ltd
• Subjects: Age; Age differences; Applications and Case Studies; autocorrelation; Bayesian analysis; Bayesian theory; Biomechanics; Blindness; Correlation; Damage assessment; Etiology; eyes; Functional data analysis; Functional mixed models; Functional regression; Functionals; Glaucoma; growth curves; Hypotheses; Intraocular pressure; Longitudinal functional data; nerve tissue; Nonparametric effects; Nonparametric statistics; Regression analysis; risk; Risk analysis; Risk factors; Smoothing splines; Smoothness; Statistical inference; Statistical methods; Statistics; Transformation; Bayesian models; Glaucoma; Smoothing Splines; Wavelets; Functional regression; Longitudinal Functional Data; Nonparametric effects; Spherical data; Functional mixed models; Functional data analysis

Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devised a custom device to measure scleral strain continuously around the eye under fixed levels of IOP, which here is used...