We start the treatment of the selection of the smoothing parameter in nonparametric regression with a discussion of optimality criteria. In the remainder of this chapter, we discuss their implementation for linear leastsquares problems, in particular for smoothing spline estimators, the polynomial sieve, and local polynomials.
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© 2009 Springer-Verlag New York
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Eggermont, P.P.B., LaRiccia, V.N. (2009). Smoothing Parameter Selection. In: Maximum Penalized Likelihood Estimation. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/b12285_7
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DOI: https://doi.org/10.1007/b12285_7
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