Bootstrap methods for stationary functional time series

DOI10.1007/s11222-016-9712-8zbMath1505.62368arXiv1610.00773OpenAlexW2529071003WikidataQ58288538 ScholiaQ58288538MaRDI QIDQ1702275

Han Lin Shang

Publication date: 28 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.00773

zbMATH Keywords

maximum entropyfunctional principal component analysisfunctional kernel regressionlong-run covariancefunctional autoregressive processplug-in bandwidth

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Nonparametric statistical resampling methods (62G09)

Related Items

Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series, Bootstrap Prediction Bands for Functional Time Series, Two-time-scale nonparametric recursive regression estimator for independent functional data, Long-Range Dependent Curve Time Series, A bootstrap-based KPSS test for functional time series, Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data, Intraday forecasts of a volatility index: functional time series methods with dynamic updating, Double bootstrapping for visualizing the distribution of descriptive statistics of functional data, Bootstrapping covariance operators of functional time series, Clustering and forecasting multiple functional time series, Computing expectations and marginal likelihoods for permutations

Uses Software

• R
• fda (R)
• refund
• meboot
• freqdom.fda

Cites Work

• A Mathematical Theory of Communication
• Bootstrap confidence intervals in functional nonparametric regression under dependence
• Uniform consistency of \(k\mathrm{NN}\) regressors for functional variables
• An innovations algorithm for the prediction of functional linear processes
• Nonparametric time series forecasting with dynamic updating
• Local linear regression for functional data
• Distance-based local linear regression for functional predictors
• Common functional principal components
• On the use of the bootstrap for estimating functions with functional data
• Improving the reliability of bootstrap tests with the fast double bootstrap
• Robust forecasting of mortality and fertility rates: a functional data approach
• Subsampling
• Resampling methods for dependent data
• Linear processes in function spaces. Theory and applications
• The local bootstrap for kernel estimators under general dependence conditions
• Adaptive bandwidth selection in the long run covariance estimator of functional time series
• Functional nonparametric model for time series: a fractal approach for dimension reduction
• On flat-top kernel spectral density estimators for homogeneous random fields
• Sieve bootstrap for functional time series
• A moment-based notion of time dependence for functional time series
• Functional data analysis.
• Nonparametric regression estimation for dependent functional data: asymptotic normality
• Nonparametric functional data analysis. Theory and practice.
• Testing stationarity of functional time series
• Determining the order of the functional autoregressive model
• Erratum of: ‘Non-parametric models for functional data, with application in regression, time-series prediction and curve discrimination’
• The Stationary Bootstrap
• Corrected Confidence Bands for Functional Data Using Principal Components
• Functional prediction of intraday cumulative returns
• Resampling Techniques for Estimating the Distribution of Descriptive Statistics of Functional Data
• A Plug‐in Bandwidth Selection Procedure for Long‐Run Covariance Estimation with Stationary Functional Time Series
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Dynamic Functional Principal Components
• On Properties of Functional Principal Components Analysis
• Functional Data Analysis for Sparse Longitudinal Data
• The impact of bootstrap methods on time series analysis
• Detection of outliers in functional time series