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Showing 1 to 7 of 7 for “"Oracle inequality"”.

  1. Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation

    In this dissertation, we study three problems: oracle inequality in high-dimensional statistics theory, graphical models, and surrogate measures in clinical trials. First, we introduce a general slow rate bound for maximum regularized likelihood estimators in Kullback-Leibler divergence. The result …

    washington Repository record for Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation (opens in a new tab)

  2. Wavelet Thresholding for Non (Necessarily) Gaussian Noise

    Soon after the discovery of orthonormal wavelets, in particular the <br>compactly supported ones, <br>these wavelets have been used for non-parametric function estimation. <br>In the literature, two main models are present. In the first model, <br>the target functions are members of some smoothness …

    freiburg-diss Repository record for Wavelet Thresholding for Non (Necessarily) Gaussian Noise (opens in a new tab)

  3. Essays on Adaptive Methods for Inference and Prediction under Dependence

    … patterns (e.g., exposure mappings). We establish oracle inequalities and corresponding adaptive rates for the estimation of the interference function. Such estimates lead to two different estimators ($\hat{\tau}^{OR}$ and $\hat{\tau}^{DR}$) for the average direct treatment effect on the treated. …

    duke Repository record for Essays on Adaptive Methods for Inference and Prediction under Dependence (opens in a new tab)

  4. Polynomial methods in statistical inference: Theory and practice

    … optimality of the proposed procedure, as well as oracle inequality in misspecified models. These results can also be viewed as provable algorithms for generalized method of moments which involves non-convex optimization and lacks theoretical guarantees.

    uiuc Repository record for Polynomial methods in statistical inference: Theory and practice (opens in a new tab)

  5. Non-asymptotic bounds for prediction problems and density estimation.

    This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the dimension of the problem. The first part answers some open questions for the binary classification problem in the framework of …

    gatech Repository record for Non-asymptotic bounds for prediction problems and density estimation. (opens in a new tab)

  6. Optimal estimation in high-dimensional and nonparametric models

    … with minimal variance among all unbiased oracle-type estimators. Our approach is based on a Poisson point process model and as an ingredient, we prove that the convex hull is a sufficient and complete statistic. No hypotheses on the boundary of the convex set are imposed. In a numerical …

    cambridge Repository record for Optimal estimation in high-dimensional and nonparametric models (opens in a new tab)