Machine Learning
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Variational Bayesian functional PCA
Computational Statistics & Data Analysis
Structural components in functional data
Computational Statistics & Data Analysis
Dimensionality reduction when data are density functions
Computational Statistics & Data Analysis
Functional density synchronization
Computational Statistics & Data Analysis
An alternative objective function for fitting regression trees to functional response variables
Computational Statistics & Data Analysis
Supervised classification for functional data: A weighted distance approach
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The problem of building a regression tree is considered when the response variable is a probability density function. Splitting criteria which are well adapted to measure the dissimilarity between densities are proposed using the Csiszar's f-divergence. The comparison between performances of trees constructed with various criteria is tackled through numerical simulations. Afterwards, a tree is constructed to predict the size distribution of a zooplankton community using a set of explanatory environmental variables. Functional PCA is used in order to interpret the main modes of variation of the size spectra around the predicted density in each terminal node. Finally, a bagging procedure is used to increase the accuracy of the tree-based model.