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2005•Chinese Journal of Applied Probability and StatistiesRequires access

Asymptotic Normality of the Improved Kernel Estimator for Regression Function under φ -mixing Samples

H Iang

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Abstract

Let {(Xi,Yi),i ≥ 1} be a strictly stationary and φ-mixing sample sequence from (X, Y) in Rd ×r R. The improved recursive kernel estimator of regression function m(x) = E(Y|X = x) is defined by Under suitable conditions, we prove the asymptotic normality of mn(2) (x).

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Let {(Xi,Yi),i ≥ 1} be a strictly stationary and φ-mixing sample sequence from (X, Y) in Rd ×r R. The improved recursive kernel estimator of regression function m(x) = E(Y|X = x) is defined by Under suitable conditions, we prove the asymptotic normality of mn(2) (x).

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Available abstract

Let {(Xi,Yi),i ≥ 1} be a strictly stationary and φ-mixing sample sequence from (X, Y) in Rd ×r R. The improved recursive kernel estimator of regression function m(x) = E(Y|X = x) is defined by Under suitable conditions, we prove the asymptotic normality of mn(2) (x).

Key concepts: Mathematics, Estimator, Asymptotic distribution, Regression function, Mixing (physics), Kernel (algebra), Statistics, Kernel regression

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