Bayesian covariance estimation and inference in latent Gaussian process models

DOI10.1016/j.stamet.2013.10.001zbMath1486.62102OpenAlexW2020776406MaRDI QIDQ1731198

Cecilia Earls, Giles Hooker

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2013.10.001

zbMATH Keywords

smoothingcovariancefunctional dataBayesian modelingfunctional regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Functional data analysis (62R10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Uses Software

• WinBUGS
• fda (R)

Cites Work

• The smallest eigenvalue of a large dimensional Wishart matrix
• Generalized functional linear models
• Functional data analysis.
• Reduced rank regression models with latent variables in Bayesian functional data analysis
• Bayesian functional ANOVA modeling using Gaussian process prior distributions
• The Dirichlet labeling process for clustering functional data
• Some matrix-variate distribution theory: Notational considerations and a Bayesian application
• Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
• Hierarchical models for assessing variability among functions
• Functional Data Analysis for Sparse Longitudinal Data
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)