Nonparametric Estimation of Variance Function for Functional Data

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dc.creatorLian, Heng
dc.date2008-12-14
dc.date.accessioned2026-07-07T12:12:46Z
dc.date.available2026-07-07T12:12:46Z
dc.descriptionThis article investigates nonparametric estimation of variance functions for functional data when the mean function is unknown. We obtain asymptotic results for the kernel estimator based on squared residuals. Similar to the finite dimensional case, our asymptotic result shows the smoothness of the unknown mean function has an effect on the rate of convergence. Our simulaton studies demonstrate that estimator based on residuals performs much better than that based on conditional second moment of the responses.
dc.identifierhttps://arxiv.org/abs/0812.2628
dc.identifierhttp://arxiv.org/abs/0812.2628
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210654
dc.subjectMethodology
dc.subjectStatistics Theory
dc.titleNonparametric Estimation of Variance Function for Functional Data
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