Results 11 to 20 of about 47 (46)
Minimax Estimation via Wavelets for Indirect Long-Memory Data
, 1997 In this paper we model linear inverse problems with long-range dependence by a fractional Gaussian noise model and study function estimation based on observations from the model. By using two wavelet-vaguelette decompositions, one for the inverse problem
Yazhen Wang
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Testability of high-dimensional linear models with nonsparse structures. [PDF]
Ann Stat, 2022 Bradic J, Fan J, Zhu Y.
europepmc +1 more source
Minimax Rules Under Zero-One Loss for a Restricted Location Parameter
, 1997 Minimax Rules Under Zero-One Loss In this paper we study the existence, structure and computation of minimax and near-minimax rules under zero-one loss for a restricted location parameter of an absolutely continuous distribution.
Gerda Kamberova +4 more
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Estimating the Reach of a Manifold via its Convexity Defect Function. [PDF]
Discrete Comput Geom, 2022 Berenfeld C +3 more
europepmc +1 more source
Sharper Bounds in Adaptive Group Testing
, 1998 Adaptive group testing in the presence of a large percentage of defectives is best done by individual testing rather than by pooling. The fraction of items which must be defective to make individual testing optimal remains unknown, and is conjectured to ...
Laura Riccio, Charles J. Colbourn
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Estimation of a Bounded Normal Mean: Modified Linear and Polynomial Estimators
, 1994 Linear \Gamma-minimax estimators for a bounded normal mean have been explored by Vidakovic and DasGupta (1992). They have found that the risk of linear \Gamma-minimax rules is close to the risk of exact \Gamma-minimax rules. However, the linear rules are
Brani Vidakovic
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MODEL ASSISTED VARIABLE CLUSTERING: MINIMAX-OPTIMAL RECOVERY AND ALGORITHMS. [PDF]
Ann Stat, 2020 Bunea F +4 more
europepmc +1 more source
We construct a broad class of generalized Bayes minimax estimators of the mean of a multivariate normal distribution with covariance equal to [sigma]2Ip, with [sigma]2 unknown, and under the invariant loss ||[delta](X)-[theta]||2/[sigma]2.
Zhou, Gongfu, Wells, Martin T.
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A unified and generalized set of shrinkage bounds on minimax Stein estimates
Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spherically symmetric density f(||x-[theta]||2), under loss ||[delta]-[theta]||2.
Fourdrinier, Dominique +1 more
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Admissibility and minimaxity of Bayes estimators for a normal mean matrix
In some invariant estimation problems under a group, the Bayes estimator against an invariant prior has equivariance as well. This is useful notably for evaluating the frequentist risk of the Bayes estimator.
Tsukuma, Hisayuki
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