Nonparametric regression estimation with general parametric error covariance
The asymptotic distribution for the local linear estimator in nonparametric regression models is established under a general parametric error covariance with dependent and heterogeneously distributed regressors. A two-step estimation procedure that incorporates the parametric information in the error covariance matrix is proposed. Sufficient conditions for its asymptotic normality are given and its efficiency relative to the local linear estimator is established. We give examples of how our results are useful in some recently studied regression models. A Monte Carlo study confirms the asymptotic theory predictions and compares our estimator with some recently proposed alternative estimation procedures.
Year of publication: |
2009
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Authors: | Martins-Filho, Carlos ; Yao, Feng |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 100.2009, 3, p. 309-333
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Publisher: |
Elsevier |
Keywords: | 62G08 62G20 Local linear estimation Asymptotic normality Mixing processes |
Saved in:
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