# The Dantzig selector: Statistical estimation when P is much larger than n

@article{Cands2007TheDS, title={The Dantzig selector: Statistical estimation when P is much larger than n}, author={Emmanuel J. Cand{\`e}s and Terence Tao}, journal={Quality Engineering}, year={2007}, volume={54}, pages={83-84} }

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, σ^2). Is it possible to estimate β reliably based on the noisy data y?

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