Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models
Hilgert, Nadine | Portier , Bruno
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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Другие темы
Statistique mathématique; Kernel density estimation; Functional autoregressive models; Erreur d'estimation; Multivariate central limit theorem; Probabilité; Martingale approach; Mathématiques appliquées; Nonparametric residuals
Язык
Английский
Тип
Journal Article
2016-10-15
2025-12-04
AGRIS AP
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