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Given any countable collection of regression procedures (e.g., kernel, spline, wavelet, local polynomial, neural nets, etc.), we show that a single adaptive procedure can be constructed to share their advantages to a great extent in terms of global squared L 2 risk. The combined procedure basically pays a price only of order 1/n for adaptation over the collection. An interesting consequence...
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