![]() To estimate β, we introduce a new estimator-we call it the Dantzig selector-which is a solution to the ℓ 1-regularization problem $$\min_\cdot\sigma,$$ where r is the residual vector y− Xβ̃ and t is a positive scalar. ![]() Is it possible to estimate β reliably based on the noisy data y? Suppose then that we have observations y= Xβ z, where β∈ R p is a parameter vector of interest, X is a data matrix with possibly far fewer rows than columns, n≪ p, and the z i’s are i.i.d. In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |