Abstract

In this thesis we study adaptive methods of estimation for two particular
types of statistical problems: regression and density estimation. For all
these problems the classes of probabilities are parameterized by real-valued
functions. In each model, the underlying function is assumed to belong to
some class of smooth functions. In practice the `true' smoothness
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