Sieve bootstrap for functional time series
From MaRDI portal
Publication:1990591
DOI10.1214/17-AOS1667zbMath1420.62392arXiv1609.06029OpenAlexW2963256730MaRDI QIDQ1990591
Publication date: 25 October 2018
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1609.06029
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) Nonparametric statistical resampling methods (62G09)
Related Items (11)
Principal Component Analysis of Spatially Indexed Functions ⋮ Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series ⋮ Bootstrap Prediction Bands for Functional Time Series ⋮ Bootstrap methods for stationary functional time series ⋮ Functional GARCH models: the quasi-likelihood approach and its applications ⋮ A bootstrap-based KPSS test for functional time series ⋮ Nonlinear autoregressive sieve bootstrap based on extreme learning machines ⋮ Double bootstrapping for visualizing the distribution of descriptive statistics of functional data ⋮ Bootstrapping covariance operators of functional time series ⋮ Testing equality of spectral density operators for functional processes ⋮ Moving block and tapered block bootstrap for functional time series with an application to the \(K\)-sample mean problem
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Fourier analysis of stationary time series in function space
- Inference for functional data with applications
- Bootstrap for dependent Hilbert space-valued random variables with application to von Mises statistics
- On the range of validity of the autoregressive sieve bootstrap
- On the CLT for discrete Fourier transforms of functional time series
- Bootstrap methods for dependent data: a review
- Weakly dependent functional data
- Time series: theory and methods.
- Baxter's inequality and convergence of finite predictors of multivariate stochastic processes
- Sharp conditions for the CLT of linear processes in a Hilbert space
- Resampling methods for dependent data
- Linear processes in function spaces. Theory and applications
- Baxter's inequality and sieve bootstrap for random fields
- Forecasting functional time series
- Testing stationarity of functional time series
- Functional methods for time series prediction: a nonparametric approach
- Estimation of the Mean of Functional Time Series and a Two-Sample Problem
- A Note on Estimation in Hilbertian Linear Models
- On the Vector Autoregressive Sieve Bootstrap
- A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
- On the Prediction of Stationary Functional Time Series
- Dynamic Functional Principal Components
- Selecting the Number of Principal Components in Functional Data
- Sequential block bootstrap in a Hilbert space with application to change point analysis
- Functional Data Analysis for Sparse Longitudinal Data
This page was built for publication: Sieve bootstrap for functional time series