Local polynomial regression smoothers with AR-error structure
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Local polynomial regression smoothers with AR-error structureFecha
2002Cita bibliográfica
Test, vol. 11, n. 2, pp. 439-464
Resumen
Consider the fixed regression model with random observation error that follows an
AR(1) correlation structure. In this paper, we study the nonparametric estimation
of the regression function and its derivatives using a modified version of estimators
obtained by weighted local polynomial fitting. The asymptotic properties of the proposed
estimators are studied; expressions for the bias and the variance/covariance
matrix of the estimators are obtained and the joint asymptotic normality is established.
In a simulation study, a better behavior of the Mean Integrated Squared
Error of the proposed regression estimator with respect to that of the classical local
polynomial estimator is observed when the correlation of the observations is large.
Palabras clave
Nonparametric estimators
Local polynomial fitting
Autoregressive process
Local polynomial fitting
Autoregressive process
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Derechos
The original publication is available at www.springerlink.com
ISSN
1133-0686