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A nonparametric regression estimator that adapts to error distribution of unknown form

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We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum likelihood estimator [Staniswalis (1989)], and hence improves on standard kernel estimators when the error distribution is not normal. We investigate the finite sample performance of our procedure on simulated data.

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en

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application/pdf

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http://eprints.lse.ac.uk/2120/1/A_Nonparametric_Regression_Estimator_that_Adapts_to_Error_Distribution_of_Unknown_Form.pdf

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