The Annals of Statistics

Semiparametric efficiency bounds for high-dimensional models

Jana Janková and Sara van de Geer

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Asymptotic lower bounds for estimation play a fundamental role in assessing the quality of statistical procedures. In this paper, we propose a framework for obtaining semiparametric efficiency bounds for sparse high-dimensional models, where the dimension of the parameter is larger than the sample size. We adopt a semiparametric point of view: we concentrate on one-dimensional functions of a high-dimensional parameter. We follow two different approaches to reach the lower bounds: asymptotic Cramér–Rao bounds and Le Cam’s type of analysis. Both of these approaches allow us to define a class of asymptotically unbiased or “regular” estimators for which a lower bound is derived. Consequently, we show that certain estimators obtained by de-sparsifying (or de-biasing) an $\ell_{1}$-penalized M-estimator are asymptotically unbiased and achieve the lower bound on the variance: thus in this sense they are asymptotically efficient. The paper discusses in detail the linear regression model and the Gaussian graphical model.

Article information

Ann. Statist., Volume 46, Number 5 (2018), 2336-2359.

Received: June 2016
Revised: August 2017
First available in Project Euclid: 17 August 2018

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J07: Ridge regression; shrinkage estimators
Secondary: 62F12: Asymptotic properties of estimators

Asymptotic efficiency high-dimensional sparsity Lasso linear regression graphical models Cramér–Rao bound Le Cam’s lemma


Janková, Jana; van de Geer, Sara. Semiparametric efficiency bounds for high-dimensional models. Ann. Statist. 46 (2018), no. 5, 2336--2359. doi:10.1214/17-AOS1622.

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Supplemental materials

  • Supplement to “Semiparametric efficiency bounds for high-dimensional models”. The supplementary material contains proofs.