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Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons

Abstract : 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. Numerous work have been devoted to building efficient estimators of $\theta$ when $p$ is much larger than $n$. In such a situation, a classical approach amounts to assume that $\theta_0$ is approximately sparse. This paper studies the minimax risks of estimation and testing over classes of $k$-sparse vectors $\theta$. These bounds shed light on the limitations due to high-dimensionality. The results encompass the problem of prediction (estimation of $X\theta$), the inverse problem (estimation of $\theta_0$) and linear testing (testing $X\theta=0$). Interestingly, an elbow effect occurs when the number of variables $k\log(p/k)$ becomes large compared to $n$. Indeed, the minimax risks and hypothesis separation distances blow up in this ultra-high dimensional setting. We also prove that even dimension reduction techniques cannot provide satisfying results in an ultra-high dimensional setting. Moreover, we compute the minimax risks when the variance of the noise is unknown. The knowledge of this variance is shown to play a significant role in the optimal rates of estimation and testing. All these minimax bounds provide a characterization of statistical problems that are so difficult so that no procedure can provide satisfying results.
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Contributor : Nicolas Verzelen <>
Submitted on : Monday, January 23, 2012 - 11:17:13 PM
Last modification on : Monday, November 23, 2020 - 10:06:46 AM
Long-term archiving on: : Tuesday, April 24, 2012 - 2:30:34 AM


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  • HAL Id : hal-00508339, version 3
  • ARXIV : 1008.0526


Nicolas Verzelen. Minimax risks for sparse regressions: Ultra-high-dimensional phenomenons. 2010. ⟨hal-00508339v3⟩



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