Compositional regression with functional response

DOI10.1016/j.csda.2018.01.018zbMath1469.62151OpenAlexW2792762756MaRDI QIDQ1662822

Renáta Talská, Jitka Machalová, Karel Hron, Eva Fišerová, Alessandra Menafoglio

Publication date: 20 August 2018

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

Full work available at URL: http://hdl.handle.net/11311/1045231

zbMATH Keywords

regression analysisdensity functionsBayes spaces\(B\)-spline representation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Linear regression; mixed models (62J05)

Related Items (max. 100)

Locally polynomial Hilbertian additive regression ⋮ Wasserstein \(F\)-tests and confidence bands for the Fréchet regression of density response curves ⋮ Orthogonal decomposition of multivariate densities in Bayes spaces and relation with their copula-based representation ⋮ Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation ⋮ Additive regression with Hilbertian responses ⋮ Additive Functional Regression for Densities as Responses ⋮ Compositional splines for representation of density functions ⋮ Additive regression for predictors of various natures and possibly incomplete Hilbertian responses ⋮ Compositional scalar-on-function regression with application to sediment particle size distributions ⋮ Logratio Approach to Distributional Modeling

Uses Software

• fda (R)
• FITPACK

Cites Work

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• Simplicial principal component analysis for density functions in Bayes spaces
• Dimensionality reduction when data are density functions
• A practical guide to splines
• Applied functional data analysis. Methods and case studies
• A Class-Kriging predictor for functional compositions with application to particle-size curves in heterogeneous aquifers
• Functional data analysis.
• Hilbert space of probability density functions based on Aitchison geometry
• Bayes Hilbert Spaces
• Regression Analysis for a Functional Response
• Bayesian-multiplicative treatment of count zeros in compositional data sets
• Preprocessing of centred logratio transformed density functions using smoothing splines

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