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Article Dans Une Revue Statistical Inference for Stochastic Processes Année : 2012

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

Résumé

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.

Dates et versions

hal-02643514 , version 1 (28-05-2020)

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Nadine Hilgert, Bruno Portier. Strong uniform consistency and asymptotic normality of a kernel based error density estimator in functional autoregressive models. Statistical Inference for Stochastic Processes, 2012, 15 (2), pp.105-125. ⟨10.1007/s11203-012-9065-7⟩. ⟨hal-02643514⟩
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