We consider the problem of model selection and estimation in situations where
the number of parameters diverges with the sample size. When the dimension is
high, an ideal method should have the oracle property [J. Amer. Statist. Assoc.
96 (2001) 1348--1360] and [Ann. Statist. 32 (2004) 928--961] which ensures the
optimal large sample performance. Furthermore, the high-dimensionality often
induces the collinearity problem, which should be properly handled by the ideal
method. Many existing variable selection methods fail to achieve both goals
simultaneously. In this paper, we propose the adaptive elastic-net that
combines the strengths of the quadratic regularization and the adaptively
weighted lasso shrinkage. Under weak regularity conditions, we establish the
oracle property of the adaptive elastic-net. We show by simulations that the
adaptive elastic-net deals with the collinearity problem better than the other
oracle-like methods, thus enjoying much improved finite sample performance.Comment: Published in at http://dx.doi.org/10.1214/08-AOS625 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
