The Annals of Statistics
- Ann. Statist.
- Volume 35, Number 6 (2007), 2313-2351.
The Dantzig selector: Statistical estimation when p is much larger than n
In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y=Xβ+z, where β∈Rp is a parameter vector of interest, X is a data matrix with possibly far fewer rows than columns, n≪p, and the zi’s are i.i.d. N(0, σ2). Is it possible to estimate β reliably based on the noisy data y?
To estimate β, we introduce a new estimator—we call it the Dantzig selector—which is a solution to the ℓ1-regularization problem
where r is the residual vector y−Xβ̃ and t is a positive scalar. We show that if X obeys a uniform uncertainty principle (with unit-normed columns) and if the true parameter vector β is sufficiently sparse (which here roughly guarantees that the model is identifiable), then with very large probability,
‖β̂−β‖ℓ22≤C2⋅2log p⋅(σ2+∑imin(βi2, σ2)).
Our results are nonasymptotic and we give values for the constant C. Even though n may be much smaller than p, our estimator achieves a loss within a logarithmic factor of the ideal mean squared error one would achieve with an oracle which would supply perfect information about which coordinates are nonzero, and which were above the noise level.
In multivariate regression and from a model selection viewpoint, our result says that it is possible nearly to select the best subset of variables by solving a very simple convex program, which, in fact, can easily be recast as a convenient linear program (LP).
Ann. Statist., Volume 35, Number 6 (2007), 2313-2351.
First available in Project Euclid: 22 January 2008
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62C05: General considerations 62G05: Estimation
Secondary: 94A08: Image processing (compression, reconstruction, etc.) [See also 68U10] 94A12: Signal theory (characterization, reconstruction, filtering, etc.)
Statistical linear model model selection ideal estimation oracle inequalities sparse solutions to underdetermined systems ℓ_1-minimization linear programming restricted orthonormality geometry in high dimensions random matrices
Candes, Emmanuel; Tao, Terence. The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35 (2007), no. 6, 2313--2351. doi:10.1214/009053606000001523. https://projecteuclid.org/euclid.aos/1201012958