Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models
2012
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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书目信息
其它主题
Mathématiques appliquées; Functional autoregressive models; Statistique mathématique; Multivariate central limit theorem; Nonparametric residuals; Probabilité; Kernel density estimation; Erreur d'estimation; Martingale approach
语言
英语
类型
Journal Article
2016-10-15
2025-12-04
AGRIS AP