Posterior simulation across nonparametric models for functional clustering

DOI10.1007/s13571-011-0014-zzbMath1230.62083OpenAlexW1997945240MaRDI QIDQ641799

Jamie L. Crandell, David B. Dunson

Publication date: 25 October 2011

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13571-011-0014-z

zbMATH Keywords

Dirichlet processvariable selectionmodel averagingfunctional data analysisnonparametric Bayeslatent classspecies samplingRJMCMC

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40) Nonparametric inference (62G99)

Related Items

Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data, Bayesian clustering of functional data using local features, Dirichlet process and its developments: a survey, Bayesian nonparametric spectral density estimation using B-spline priors

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Semiparametric Bayesian inference for stochastic frontier models
• Nonparametric regression using Bayesian variable selection
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator
• Ferguson distributions via Polya urn schemes
• Prior distributions on spaces of probability measures
• Bayesian estimation of free-knot splines using reversible jumps.
• Spline adaptation in extended linear models
• Exchangeable and partially exchangeable random partitions
• Prediction rules for exchangeable sequences related to species sampling
• A Bayesian analysis of some nonparametric problems
• Bayesian Regression with Multivariate Linear Splines
• Functional Linear Discriminant Analysis for Irregularly Sampled Curves
• Bayesian curve-fitting with free-knot splines
• Variable selection in clustering via Dirichlet process mixture models
• Hybrid Dirichlet Mixture Models for Functional Data
• The Nested Dirichlet Process
• Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• A Nonparametric Bayesian Modeling Approach for Cytogenetic Dosimetry
• Wavelet-based Functional Mixed Models
• Functional Clustering by Bayesian Wavelet Methods
• A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal  -hCG profiles
• Bayesian nonparametric functional data analysis through density estimation
• Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
• Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes
• Nonparametric Bayes local partition models for random effects
• A semiparametric Bayesian model for randomised block designs
• Bayesian curve fitting using multivariate normal mixtures
• A Predictive Approach to Model Selection
• Estimating Normal Means with a Dirichlet Process Prior
• Dirichlet Process Mixed Generalized Linear Models
• Bayesian mixture of splines for spatially adaptive nonparametric regression
• Semiparametric Nonlinear Mixed-Effects Models and Their Applications
• Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines
• Clustering for Sparsely Sampled Functional Data
• Independent and Identically Distributed Monte Carlo Algorithms for Semiparametric Linear Mixed Models
• Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models
• Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves
• A Semiparametric Bayesian Approach to the Random Effects Model
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• Bayesian Measures of Model Complexity and Fit
• Exact and approximate sum representations for the Dirichlet process
• Gibbs Sampling Methods for Stick-Breaking Priors
• Estimating normal means with a conjugate style dirichlet process prior
• Bayesian Density Estimation and Inference Using Mixtures
• Bayesian Adaptive Regression Splines for Hierarchical Data
• Monte Carlo sampling methods using Markov chains and their applications
• A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes