Functional data clustering using principal curve methods
From MaRDI portal
Publication:5104527
DOI10.1080/03610926.2021.1872636OpenAlexW3120001221MaRDI QIDQ5104527
Publication date: 14 September 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2021.1872636
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Model-based clustering of time series in group-specific functional subspaces
- Determination of parameters of a functional relation by factor analysis
- Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
- Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
- PCA stability and choice of dimensionality
- Model-based clustering of high-dimensional data: a review
- A survey of functional principal component analysis
- Clustering functional data
- Weak convergence for the covariance operators of a Hilbertian linear process.
- Defining probability density for a distribution of random functions
- Functional data clustering: a survey
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Properties of principal component methods for functional and longitudinal data analysis
- Some Statistical Methods for Comparison of Growth Curves
- Clustering Using Objective Functions and Stochastic Search
- Unsupervised Curve Clustering using B‐Splines
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Clustering for Sparsely Sampled Functional Data
- A family of minimax rates for density estimators in continuous time
- Principal Curves
- Bayes Factors
- Functional Data Analysis for Sparse Longitudinal Data
- Another look at principal curves and surfaces
This page was built for publication: Functional data clustering using principal curve methods
