Uniform convergence and rate adaptive estimation of convex functions via constrained optimization (Q2862446)

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scientific article; zbMATH DE number 6227445
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Uniform convergence and rate adaptive estimation of convex functions via constrained optimization
scientific article; zbMATH DE number 6227445

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    15 November 2013
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    convex regression
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    shape restricted nonparametric estimation
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    B-splines
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    minimax theory
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    adaptive estimation
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    constrained optimization
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    asymptotic analysis
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    Uniform convergence and rate adaptive estimation of convex functions via constrained optimization (English)
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    Convex estimators and B-spline estimators are constructed which are either adaptive with respect to the Hölder exponent \(r\) or are chosen for such a fixed exponent. The background for this work lies in the interest in asymptotic analysis and adaptive convex estimators for the aforementioned Hölder space using uniform norms. Splines and concretely B-splines turn out to be most useful for this paper. Indeed, for the B-spline case optimal rates of convergence are established. For these purposes, uniform Lipschitz properties in the uniform norm of optimal B-spline coefficients are derived in advance, as well as a formulation of the estimator and its coefficients in terms of constrained optimisation. A number of numerical examples are included at the end to illustrate the results.
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