Test of independence for functional data

DOI10.1016/j.jmva.2013.02.005zbMath1277.62124OpenAlexW2131123148MaRDI QIDQ391591

Marie Hušková, Lajos Horváth, Gregory Rice

Publication date: 10 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.02.005

zbMATH Keywords

asymptotic normalitysample autocovariancesKarhunen-Loéve expansionsvariables in Hilbert spaces

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Applications of functional analysis in probability theory and statistics (46N30)

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Uses Software

• fda (R)

Cites Work

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• Portmanteau Test of Independence for Functional Observations
• Inference for functional data with applications
• A normal approximation for the chi-square distribution
• Curve forecasting by functional autoregression
• Weakly dependent functional data
• Time series: theory and methods
• Classes of linear operators. Vol. I
• Weak convergence for the covariance operators of a Hilbertian linear process.
• On the dependence of the Berry-Esseen bound on dimension
• Weak invariance principles for sums of dependent random functions
• Generalized functional linear models
• Functional data analysis.
• Improved multivariate portmanteau test
• A Functional Wavelet–Kernel Approach for Time Series Prediction
• The Multivariate Portmanteau Statistic
• Distribution of Multivariate White Noise Autocorrelations
• On a measure of lack of fit in time series models
• Normal Approximation
• A Powerful Portmanteau Test of Lack of Fit for Time Series
• A family of minimax rates for density estimators in continuous time
• Detecting Changes in the Mean of Functional Observations
• Testing the Equality of Covariance Operators in Functional Samples
• Estimation of the Mean of Functional Time Series and a Two-Sample Problem
• Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles
• Tests for Error Correlation in the Functional Linear Model
• Multivariate Portmanteau Test For Autoregressive Models with Uncorrelated but Nonindependent Errors
• The Existence of Probability Measures with Given Marginals
• A Normal Approximation for Binomial, F, Beta, and Other Common, Related Tail Probabilities, I
• Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models