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- W485232 abstract "High-dimensional regression is about the case p n. In that situation, a certain amount of complexity regularization is needed, because e.g. ordinary least squares will lead to overfitting. We will address the question: how to choose the complexity penalty? Here, we use results for the modulus of continuity of the empirical process. This approach will allow us to extend the situation to high-dimensional nonlinear regression Yi = p ∑" @default.
- W485232 created "2016-06-24" @default.
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- W485232 date "2008-01-01" @default.
- W485232 modified "2023-09-27" @default.
- W485232 title "On complex models and weighted empirical processes" @default.
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