Statistical Science

A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers

Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright, and Bin Yu

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High-dimensional statistical inference deals with models in which the the number of parameters $p$ is comparable to or larger than the sample size $n$. Since it is usually impossible to obtain consistent procedures unless $p/n\rightarrow0$, a line of recent work has studied models with various types of low-dimensional structure, including sparse vectors, sparse and structured matrices, low-rank matrices and combinations thereof. In such settings, a general approach to estimation is to solve a regularized optimization problem, which combines a loss function measuring how well the model fits the data with some regularization function that encourages the assumed structure. This paper provides a unified framework for establishing consistency and convergence rates for such regularized $M$-estimators under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive some existing results, and also to obtain a number of new results on consistency and convergence rates, in both $\ell_{2}$-error and related norms. Our analysis also identifies two key properties of loss and regularization functions, referred to as restricted strong convexity and decomposability, that ensure corresponding regularized $M$-estimators have fast convergence rates and which are optimal in many well-studied cases.

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Statist. Sci., Volume 27, Number 4 (2012), 538-557.

First available in Project Euclid: 21 December 2012

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High-dimensional statistics $M$-estimator Lasso group Lasso sparsity $\ell_{1}$-regularization nuclear norm


Negahban, Sahand N.; Ravikumar, Pradeep; Wainwright, Martin J.; Yu, Bin. A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers. Statist. Sci. 27 (2012), no. 4, 538--557. doi:10.1214/12-STS400.

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Supplemental materials

  • Supplementary material: Supplementary material for “A unified framework for high-dimensional analysis of $\boldsymbol{M} $-estimators with decomposable regularizers”. Due to space constraints, the proofs and technical details have been given in the supplementary document by Negahban et al. [49].