Non-parametric regression on compositional covariates using Bayesian P-splines

DOI10.1007/s10260-015-0339-2zbMath1416.62208OpenAlexW1852043389MaRDI QIDQ290353

Francesca Bruno, Fedele Greco, Massimo Ventrucci

Publication date: 1 June 2016

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11585/555224

zbMATH Keywords

compositional dataBayesian P-splinesintrinsinc Gaussian Markov random fieldsisometric log-ratiovegetation cover

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to environmental and related topics (62P12)

Related Items (6)

Convergence properties for the partial sums of widely orthant dependent random variables under some integrable assumptions and their applications ⋮ Editorial: Advanced methods for space-time environmental data ⋮ A review of compositional data analysis and recent advances ⋮ A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes ⋮ Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes ⋮ Sliced inverse regression method for multivariate compositional data modeling

Uses Software

• gss
• SemiPar
• gamair

Cites Work

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• Isometric logratio transformations for compositional data analysis
• Fast and compact smoothing on large multidimensional grids
• Smooth-CAR mixed models for spatial count data
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Generalized structured additive regression based on Bayesian P-splines
• Global bifurcations and multi-pulse chaotic dynamics of rectangular thin plate with one-to-one internal resonance
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Generalized Linear Array Models with Applications to Multidimensional Smoothing
• Semiparametric Regression
• Bayesian Measures of Model Complexity and Fit
• Gaussian Markov Random Fields
• P-spline ANOVA-type interaction models for spatio-temporal smoothing
• Non-parametric regression for compositional data

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