Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models

Hilgert , Nadine
        INRA
        , Montpellier .
         UMR 0729 Mathématiques, Informatique et Statistique pour l'Environnement et l'Agronomie; Portier  , Bruno 
        . LMI EA 3226

Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models

2012

Hilgert , Nadine (INRA , Montpellier (France). UMR 0729 Mathématiques, Informatique et Statistique pour l'Environnement et l'Agronomie ) | Portier , Bruno (Institut National des Sciences Appliquées, Saint-Etienne du Rouvray(France). LMI EA 3226)

Estimating the innovation probability density is an important issue in any regression analysis. This paper focuses on functional autoregressive models. A residual-based kernel estimator is proposed for the innovation density. Asymptotic properties of this estimator depend on the average prediction error of the functional autoregressive function. Sufficient conditions are studied to provide strong uniform consistency and asymptotic normality of the kernel density estimator.

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modèle mathématique modèle statistique

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其它主题

Statistique mathématique; Kernel density estimation; Functional autoregressive models; Erreur d'estimation; Multivariate central limit theorem; Probabilité; Martingale approach; Mathématiques appliquées; Nonparametric residuals

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英语

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Journal Article

来源

Statistical inference for stochastic processes, 15, 105-125

自何时收录于AGRIS: 2014-06-15

修改日期: 2025-12-04

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Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models

2012

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书目信息