Compositional splines for representation of density functions
DOI10.1007/s00180-020-01042-7zbMath1505.62264arXiv1905.06858OpenAlexW3094421819MaRDI QIDQ2032204
Renáta Talská, Aleš Gába, Karel Hron, Jitka Machalová
Publication date: 16 June 2021
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1905.06858
smoothing splineconstrained approximationspline representationsimplicial functional principal component analysis
Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Numerical computation using splines (65D07) Functional data analysis (62R10)
Related Items (3)
Uses Software
Cites Work
- Simplicial principal component analysis for density functions in Bayes spaces
- Dimensionality reduction when data are density functions
- Isometric logratio transformations for compositional data analysis
- A comment on the orthogonalization of B-spline basis functions and their derivatives
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- A practical guide to splines
- Compositional regression with functional response
- Functional data analysis.
- Hilbert space of probability density functions based on Aitchison geometry
- Bayes Hilbert Spaces
- Functional Data Analysis with R and MATLAB
- Bayesian-multiplicative treatment of count zeros in compositional data sets
- Preprocessing of centred logratio transformed density functions using smoothing splines
- Geometric approach to statistical analysis on the simplex
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Compositional splines for representation of density functions
