Identifying the finite dimensionality of curve time series
DOI10.1214/10-AOS819zbMath1204.62152arXiv1211.2522MaRDI QIDQ620552
Qiwei Yao, Neil Bathia, Flávio Augusto Ziegelmann
Publication date: 19 January 2011
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1211.2522
dimension reductionautocovarianceeigenanalysisKarhunen-Loève expansion\(n\) convergence ratecurve time seriesroot-\(n\) convergence rate
Nonparametric regression and quantile regression (62G08) Multivariate analysis (62H99) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09) Eigenvalues, singular values, and eigenvectors (15A18) Stochastic processes (60G99)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
- Common functional principal components
- Robust forecasting of mortality and fertility rates: a functional data approach
- Principal components analysis of sampled functions
- Linear processes in function spaces. Theory and applications
- Functional data analysis.
- Nonparametric functional data analysis. Theory and practice.
- Assessing the Finite Dimensionality of Functional Data
- Bivariate splines for spatial functional regression models
- Modelling multiple time series via common factors
- Identifying a Simplifying Structure in Time Series
- Inference for Density Families Using Functional Principal Component Analysis
- Limiting behavior of regular functionals of empirical distributions for stationary *-mixing processes