Asymptotic properties of principal component projections with repeated eigenvalues
From MaRDI portal
Publication:2407519
DOI10.1016/j.spl.2017.07.004zbMath1381.62149OpenAlexW2741098481MaRDI QIDQ2407519
Justin Petrovich, Matthew Reimherr
Publication date: 6 October 2017
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2017.07.004
Factor analysis and principal components; correspondence analysis (62H25) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)
Related Items
Optimal function-on-scalar regression over complex domains, A note on strong-consistency of componentwise ARH(1) predictors, Some Remarks on the Nelson–Siegel Model
Uses Software
Cites Work
- Unnamed Item
- An introduction to recent advances in high/infinite dimensional statistics
- Optimal eigen expansions and uniform bounds
- Optimal estimates for the rate of strong Gaussian approximate in a Hilbert space
- A partial overview of the theory of statistics with functional data
- Test of independence for functional data
- A functional central limit theorem for Hilbert-valued martingales
- Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
- Functional regression with repeated eigenvalues
- Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
- Multilevel functional principal component analysis
- Functional data analysis
- Sharp conditions for the CLT of linear processes in a Hilbert space
- On the central limit theorem and its weak invariance principle for strongly mixing sequences with values in a Hilbert space via martingale approximation
- A survey of functional principal component analysis
- Asymptotic normality of the principal components of functional time series
- Generalized functional linear models
- Functional data clustering: a survey
- Nonparametric functional data analysis. Theory and practice.
- Properties of principal component methods for functional and longitudinal data analysis
- High-dimensional principal projections
- Classical testing in functional linear models
- Central limit theorems for Hilbert-space valued random fields satisfying a strong mixing condition
- Generalized Multilevel Functional Regression
- Testing Hypotheses in the Functional Linear Model
- A family of minimax rates for density estimators in continuous time
- Introduction to Functional Data Analysis
- Corrected Confidence Bands for Functional Data Using Principal Components
- A Note on Estimation in Hilbertian Linear Models
- Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
- Dynamic Functional Principal Components
- On Properties of Functional Principal Components Analysis
- Achieving near Perfect Classification for Functional Data