Spline-backfitted kernel smoothing of partially linear additive model |
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Authors: | Shujie MaLijian Yang |
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Affiliation: | Department of Statistics and Probability, Michigan State University, USA |
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Abstract: | A spline-backfitted kernel smoothing method is proposed for partially linear additive model. Under assumptions of stationarity and geometric mixing, the proposed function and parameter estimators are oracally efficient and fast to compute. Such superior properties are achieved by applying to the data spline smoothing and kernel smoothing consecutively. Simulation experiments with both moderate and large number of variables confirm the asymptotic results. Application to the Boston housing data serves as a practical illustration of the method. |
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Keywords: | Bandwidths B spline Knots Local linear estimator Mixing Nadaraya-Watson estimator Nonparametric regression |
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