Summary
Additive regression models have been shown to be useful in many situations. Numerical estimation of these models is usually done using the iterative back-fitting technique. This paper proposes an estimator for additive models with an explicit ‘hat matrix’ which does not use iteration. The asymptotic normality of the estimator is proved. We also investigate a variable selection procedure using the proposed estimator and prove that asymptotically the procedure finds the correct variable set with probability 1. A simulation study is presented investigating the practical performance of the procedure.
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© 1996 Physica-Verlag Heidelberg
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Chen, R., Härdle, W., Linton, O.B., Severance-Lossin, E. (1996). Nonparametric Estimation of Additive Separable Regression Models. In: Härdle, W., Schimek, M.G. (eds) Statistical Theory and Computational Aspects of Smoothing. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-48425-4_18
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DOI: https://doi.org/10.1007/978-3-642-48425-4_18
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-0930-5
Online ISBN: 978-3-642-48425-4
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