Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints

DOI10.1016/j.jmva.2018.10.007zbMath1416.62408OpenAlexW2898196273WikidataQ129035072 ScholiaQ129035072MaRDI QIDQ1733283

Jacopo Rossini, Antonio Canale

Publication date: 21 March 2019

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

Full work available at URL: http://hdl.handle.net/11577/3288712

zbMATH Keywords

demand and offer modelfunctional bootstrapfunctional ridge regression

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Hypothesis testing in multivariate analysis (62H15) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09)

Related Items (3)

On seasonal functional modeling under strong dependence, with applications to mechanically ventilated breathing activity ⋮ Recent advances in functional data analysis and high-dimensional statistics ⋮ Bayesian functional forecasting with locally-autoregressive dependent processes

Uses Software

• R
• fda (R)
• KernSmooth
• mctoolbox
• ElemStatLearn
• bootstrap

Cites Work

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