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2011•Journal of Hefei University of TechnologyRequires access

Asymptotic normality of modified kernel regression estimator for α-mixing functional data

Nengxiang Ling

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Abstract

In this paper,the regression model Y=r(X)+e is investigated.{(Xi,Yi),1≤i≤n} is set as the random vectors assumed to be identically distributed values taken in E×R,where E is a certain Abstract semi-metric space endowed with a semi-metric d(·,·) and R is a real space.The asymptotic normality of a modified kernel regression estimator for α-mixing functional data is established by employing Bernstein's big-block and small-block procedure.

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What this paper is about

In this paper,the regression model Y=r(X)+e is investigated.{(Xi,Yi),1≤i≤n} is set as the random vectors assumed to be identically distributed values taken in E×R,where E is a certain Abstract semi-metric space endowed with a semi-metric d(·,·) and R is a real space.The asymptotic normality of a modified kernel regression estimator for α-mixing functional data is established by employing Bernstein's big-block and small-block procedure.

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Available abstract

In this paper,the regression model Y=r(X)+e is investigated.{(Xi,Yi),1≤i≤n} is set as the random vectors assumed to be identically distributed values taken in E×R,where E is a certain Abstract semi-metric space endowed with a semi-metric d(·,·) and R is a real space.The asymptotic normality of a modified kernel regression estimator for α-mixing functional data is established by employing Bernstein's big-block and small-block procedure.

Key concepts: Mathematics, Estimator, Asymptotic distribution, Kernel (algebra), Mixing (physics), Applied mathematics, Metric space, Statistics

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