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• W2056541774 abstract "It is well-known that pure nonparametric techniques perform less and less well when the dimension of the regression function increases, because of the sparsness of the data. We attack this curse of dimensionality by proposing a method to select a set of regressors. Optimality with respect to quadratic loss function is shown. Then, simulated examples are presented to illustrate both curse of dimensionality and behaviour of the variables selection procedure." @default.
• W2056541774 created "2016-06-24" @default.
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• W2056541774 date "1994-06-01" @default.
• W2056541774 modified "2023-10-03" @default.
• W2056541774 title "Choice of regressors in nonparametric estimation" @default.
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• W2056541774 doi "https://doi.org/10.1016/0167-9473(94)90149-x" @default.
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