Sparsely observed functional time series: estimation and prediction

DOI10.1214/20-EJS1690zbMath1439.62194arXiv1811.06340OpenAlexW3101084562MaRDI QIDQ2180058

Tomáš Rubín, Victor M. Panaretos

Publication date: 13 May 2020

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

Full work available at URL: https://arxiv.org/abs/1811.06340

zbMATH Keywords

nonparametric regressionfunctional data analysisconfidence bandsspectral density operatorautocovariance operator

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Nonparametric tolerance and confidence regions (62G15) Stationary stochastic processes (60G10) Inference from stochastic processes and spectral analysis (62M15)

Related Items (5)

Inference and Computation for Sparsely Sampled Random Surfaces ⋮ Factor models for high‐dimensional functional time series I: Representation results ⋮ Functional lagged regression with sparse noisy observations ⋮ Functional estimation of anisotropic covariance and autocovariance operators on the sphere ⋮ Preprocessing noisy functional data: a multivariate perspective

Uses Software

• fda (R)
• freqdom.fda

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