By the late 1980 s, Classical nonparametrics was established as “classical”. Concurrently, however, the beginnings of a different stream of nonparametric thinking were already under way. Indeed, its origins go back to the 1970 s if not earlier. The focus here is not on large spaces of functions but on classes of functions intended to be tractable representations for intermediate tranches. The models retain much of the flexibility of Classical methods but are much more interpretable; not as interpretable as many subject matter specialists might want but possessing much more structure than themethods of Chapters 2 and 3. In practice, computer-intensive procedures pervade these more recent techniques. This permits iterative fitting algorithms, cross-validation for model selection, bootstrapping for pointwise confidence bands on the estimated functions as seen earlier, and much more besides.
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© 2009 Springer-Verlag New York
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Clarke, B., Fokoué, E., Zhang, H.H. (2009). New Wave Nonparametrics. In: Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-98135-2_4
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DOI: https://doi.org/10.1007/978-0-387-98135-2_4
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