Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach

DOI10.1214/11-EJS662zbMath1274.62412arXiv0807.1106MaRDI QIDQ1952256

Jie Peng, Debashis Paul

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0807.1106

zbMATH Keywords

consistencykernel smoothingprincipal component analysisfunctional data analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Asymptotic properties of nonparametric inference (62G20)

Related Items

Mean and Covariance Estimation for Functional Snippets, Maximal autocorrelation functions in functional data analysis, Sparsely observed functional time series: estimation and prediction, Test of Weak Separability for Spatially Stationary Functional Field, Functional principal component analysis of spatially correlated data, Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach, Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies, Fast implementation of partial least squares for function-on-function regression

Uses Software

• fda (R)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Functional linear regression analysis for longitudinal data
• A reproducing kernel Hilbert space approach to functional linear regression
• Prediction in functional linear regression
• Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
• Methodology and convergence rates for functional linear regression
• Consistency of restricted maximum likelihood estimators of principal components
• Changes in atmospheric radiation from the statistical point of view
• Simultaneous non-parametric regressions of unbalanced longitudinal data
• Functional linear model
• Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach
• Nonstationary multivariate process modeling through spatially varying coregionalization
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• Properties of principal component methods for functional and longitudinal data analysis
• Statistical inferences for functional data
• Functional Linear Discriminant Analysis for Irregularly Sampled Curves
• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• Lectures on the Combinatorics of Free Probability
• Coregionalized Single- and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth
• Inference for Density Families Using Functional Principal Component Analysis
• Clustering for Sparsely Sampled Functional Data
• Nonparametric estimation of smoothed principal components analysis of sampled noisy functions
• Principal component models for sparse functional data
• Penalized Spline Models for Functional Principal Component Analysis
• Functional Data Analysis for Sparse Longitudinal Data