1990
DOI: 10.1016/0885-064x(90)90025-9
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Minimax quadratic estimation of a quadratic functional

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Cited by 102 publications
(69 citation statements)
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“…In other words, for the Sh j (ε) calculation, we use a white noise with the same variance σ 2 to replace the white noise ε in the noisy LIBS signal. The estimation method about the variance σ 2 of the white noise ε is proposed by Donoho [45].…”
Section: Choice With Entropy Analysismentioning
confidence: 99%
“…In other words, for the Sh j (ε) calculation, we use a white noise with the same variance σ 2 to replace the white noise ε in the noisy LIBS signal. The estimation method about the variance σ 2 of the white noise ε is proposed by Donoho [45].…”
Section: Choice With Entropy Analysismentioning
confidence: 99%
“…Donoho and Nussbaum [6] proposed an estimator of λ∈N * β 2 λ in the Gaussian sequence model, with the prior information that the sequence (β λ , λ ∈ N * ) belongs to some ellipsoïd.…”
mentioning
confidence: 99%
“…This is another sense in which success of renormalization entails quasi-linearity. We know that already for global quadratic functionals, the diculty o f the full problem can fail to be equivalent to the diculty of the hardest two-point subproblem; see the discussion in [5]. Hence we expect that the renormalization approach of this paper fails in such cases.…”
Section: Hardest Subproblems and Renormalizationmentioning
confidence: 94%