Bayesian Functional Data Analysis Using WinBUGS.
Reads0
Chats0
TLDR
This paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.Abstract:
We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.read more
Citations
More filters
Journal ArticleDOI
Statistical Computing in Functional Data Analysis: The R Package fda.usc
TL;DR: This paper is devoted to the R package fda.usc which includes some utilities for functional data analysis which carries out exploratory and descriptive analysis of functional data analyzing its most important features such as depth measurements or functional outliers detection, among others.
Posted Content
Functional Regression
TL;DR: Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid.
Journal ArticleDOI
Penalized Functional Regression.
TL;DR: Differences between various cerebral white-matter tract property measurements of multiple sclerosis patients and controls are analyzed to analyze differences between various Cerebral White-matter demyelination via diffusion tensor imaging (DTI).
Journal ArticleDOI
Functional mixed effects models
Ziyue Liu,Wensheng Guo +1 more
TL;DR: A new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced, which inherit the flexibility of the linear mixed effects models in handling complex designs and correlation structures.
Journal ArticleDOI
Corrected confidence bands for functional data using principal components.
TL;DR: This article proposes a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions, and applies this method to sparse observations of CD4 cell counts and to dense white-matter tract profiles.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman,Donald B. Rubin +1 more
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Book
Generalized Additive Models: An Introduction with R, Second Edition
TL;DR: In this article, a simple linear model is proposed to describe the geometry of linear models, and a general linear model specification in R is presented. But the theory of linear model theory is not discussed.
BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Journal ArticleDOI
Generalized Additive Models: An Introduction With R
TL;DR: Robinson, R. (2007). Generalized Additive Models: An Introduction With R.(2007).