A flexible approach to Bayesian multiple curve fitting

DOI10.1016/J.CSDA.2008.05.008zbMath1452.62035OpenAlexW2092003129WikidataQ34362323 ScholiaQ34362323MaRDI QIDQ1023881

Carsten H. Botts, Michael J. Daniels

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2994020

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Bayesian inference (62F15)

Related Items (3)

A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case-Control Studies ⋮ Bayesian Hierarchical Poisson Regression Models: An Application to a Driving Study With Kinematic Events ⋮ Semiparametric stochastic frontier models for clustered data

Uses Software

• WinBUGS

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Multivariate adaptive regression splines
• A practical guide to splines
• Polynomial splines and their tensor products in extended linear modeling. (With discussions)
• Bayesian curve-fitting with free-knot splines
• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• The Schwarz criterion and related methods for normal linear models
• Automatic Bayesian Curve Fitting
• Air Pollution and Mortality
• Principal component models for sparse functional data
• Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves
• Iterative Weighted Least Squares Estimation in Heteroscedastic Linear Models
• An Analysis of Paediatric CD4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves
• Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes
• Mixed Models
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Bayesian Adaptive Regression Splines for Hierarchical Data
• Hierarchical models for assessing variability among functions
• Misinformation in the conjugate prior for the linear model with implications for free-knot spline modelling
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