Nonparametric forecasting: a comparison of three kernel-based methods

DOI10.1080/03610929808832180zbMath0930.62037OpenAlexW2039540767MaRDI QIDQ4214004

Ali Gannoun, Eric Matzner-Løber, Jan G. De Gooijer

Publication date: 13 February 2000

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929808832180

zbMATH Keywords

smoothingbandwidthtime seriesmeanmediankernel methodconditional densitymode\(k\)-Markovian

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07)

Related Items (9)

Mean squared error properties of the kernel-based multi-stage median predictor for time series ⋮ A Statistical Learning Approach to Modal Regression ⋮ MULTI-STAGE KERNEL-BASED CONDITIONAL QUANTILE PREDICTION IN TIME SERIES ⋮ Nonparametric prediction by conditional median and quantiles ⋮ Semiparametric model average prediction in panel data analysis ⋮ Functional methods for time series prediction: a nonparametric approach ⋮ Information geometry of modal linear regression ⋮ Nonparametric conditional predictive regions for time series ⋮ Normalité asymptotique d'estimateurs convergents du mode conditionnel

Uses Software

• alr3

Cites Work

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• Some mixing properties of time series models
• Bilinear Markovian representation and bilinear models
• Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes
• Mixing: Properties and examples
• A note on prediction via estimation of the conditional mode function
• Optimal global rates of convergence for nonparametric regression
• [https://portal.mardi4nfdi.de/wiki/Publication:3038407 Propri�t�s de convergence presque compl�te du pr�dicteur � noyau]
• A Look at Some Data on the Old Faithful Geyser
• KERNEL REGRESSION SMOOTHING OF TIME SERIES
• Non‐parametric vs parametric forecasting in time series: A computational point of view

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