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Consider the standard Gaussian linear regression model $Y=X\theta+\epsilon$, where $Y\in R^n$ is a response vector and $ X\in R^{n*p}$ is a design matrix.
Kernel dimension reduction in regression
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Non-asymptotic rates of testing in signal detection
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Dimension reduction for conditional mean in regression
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Adaptive detection of a signal of growing dimension II
Ingster, Y · 2002
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Adaptive tests of linear hypotheses by model selection
Baraud, Y · 2003
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Concentration inequalities and model selection . Lecture Notes in Mathematics, Vol. 1896
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Near-ideal model selection by ℓ 1 \ell_{1} minimization
Candès, E · 2009
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A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism
Chu, J.-H · 2009
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Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
Donoho, D · 2009
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Minimax rates of estimations for high-dimensional regression over l q l_{q} balls
Raskutti, G · 2009
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Wainwright, M · 2009
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Technical Appendix to ”Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons.”
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