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- W2052844175 abstract "Robust nonparametric estimators for additive regression or autoregression models under an α-mixing condition are proposed. They are based on local M-estimators or local medians with kernel weights, and their asymptotic behaviour is studied. Moreover, diese local M-estimators achieve the same univariate rate of convergence as their linear relatives. Nous proposons des estimateurs robustes pour des modèles de regression ou d'autoregression additifs pour des processus α-mélangeants. Les estimateurs étudiés sont des M-estimateurs locaux et des médianes locales, considérant comme famille de poids les noyaux. On établit des résultats concernant leur loi de distribution asymptotique qui entraǐnent que ces estimateurs ont la měme vitesse de convergence univariée que les estimateurs linéaires associés." @default.
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- W2052844175 date "1998-06-01" @default.
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- W2052844175 title "Robust kernel estimators for additive models with dependent observations" @default.
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- W2052844175 doi "https://doi.org/10.2307/3315508" @default.
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