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

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

2009-05-14

arxiv.org/abs/0905.2327v2

Statistics

Methodology

Scientific paper

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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