We consider the model y = Xθ* + ξ, Z = X + Ξ, where the random vector y ∈ ℝ n and the random n × p matrix Z are observed, the n × p matrix X is unknown, Ξ is an n × p random noise matrix, ξ ∈ ℝ n is a ...
We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization, and its adaptive version, for achieving sparsity when estimating the dimensions of a central subspace ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback