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Consider the standard linear regression model $\y = \Xmat \betastar + w$, where $\y \in \real^\numobs$ is an observation vector, $\Xmat \in \real^{\numobs \times \pdim}$ is a design matrix, $\betastar \in \real^\pdim$ is the unknown regression vector, and $w \sim \mathcal{N}(0, \sigma^2 I)$ is additive Gaussian noise.
A lower bound on the risks of nonparametric estimates of densities in the uniform metric
R. Z. Has’minskii · 1978
Earlier work this paper cites.
Optimal filtering of square integrable signals in gaussian white noise
M. S. Pinsker · 1980
Earlier work this paper cites.
Statistical Estimation: Asymptotic Theory
I. A. Ibragimov and R. Z. Has’minskii · 1981
Earlier work this paper cites.
Convergence of Stochastic Processes
D. Pollard · 1984
Earlier work this paper cites.
Entropy numbers of diagonal operators between symmetric Banach spaces
C. Schütt · 1984
Earlier work this paper cites.
Some inequalities for Gaussian processes and applications
Y. Gordon · 1985
Earlier work this paper cites.
Gelfand numbers of operators with values in a Hilbert space
B. Carl and A. Pajor · 1988
Earlier work this paper cites.
Entropy, compactness and the approximation of operators
B. Carl and I. Stephani · 1990
Earlier work this paper cites.
Elements of Information Theory
T.M. Cover and J.A. Thomas · 1991
Earlier work this paper cites.
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M. Ledoux and M. Talagrand · 1991
Earlier work this paper cites.
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D. L. Donoho and I. M. Johnstone · 1994
Earlier work this paper cites.
Asymptotic equivalence of nonparametric regression and white noise
L. Brown and M. Low · 1996
Earlier work this paper cites.
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Earlier work this paper cites.
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