SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax - implementation -

SLOPE is Adaptive to Unknown Sparsity and Asymptotically Minimax by Weijie Su, Emmanuel Candes

We consider high-dimensional sparse regression problems in which we observe $y = X \beta + z$, where $X$ is an $n \times p$ design matrix and $z$ is an $n$-dimensional vector of independent Gaussian errors, each with variance $\sigma^2$. Our focus is on the recently introduced SLOPE estimator (Bogdan et al., 2014), which regularizes the least-squares estimates with the rank-dependent penalty $\sum_{1 \le i \le p} \lambda_i |\hat \beta|_{(i)}$, where $|\hat \beta|_{(i)}$ is the $i$th largest magnitude of the fitted coefficients. Under Gaussian designs, where the entries of $X$ are i.i.d. $\mathcal{N}(0, 1/n)$, we show that SLOPE, with weights $\lambda_i$ just about equal to $\sigma \cdot \Phi^{-1}(1-iq/(2p))$ ($\Phi^{-1}(\alpha)$ is the $\alpha$th quantile of a standard normal and $q$ is a fixed number in $(0,1)$) achieves a squared error of estimation obeying \[ \sup_{\|\beta\|_0 \le k} \,\, \mathbb{P}\left(\| \hat\beta_{SLOPE} - \beta \|^2 > (1+\epsilon) \, 2\sigma^2 k \log(p/k) \right) \longrightarrow 0 \] as the dimension $p$ increases to $\infty$, and where $\epsilon > 0$ is an arbitrary small constant. This holds under weak assumptions on the sparsity level $k$ and is sharp in the sense that this is the best possible error {\em any} estimator can achieve. A remarkable feature is that SLOPE does not require any knowledge of the degree of sparsity, and yet automatically adapts to yield optimal total squared errors over a wide range of sparsity classes. We are not aware of any other estimator with this property.

Code and data are available from the paper's webpage.

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