In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to flexible neural network architecture, our methodology uses dimension reduction and feature selection from set instruments covariates. A central theoretical result, due Brillinger (2012) Selected Works Daving Brillinger. 589-606, shows that pro...

Least squares regression (LSR) is an effective method that has been widely used for subspace clustering. Under the conditions of independent subspaces and noise-free data, coefficient matrices can satisfy enforced block diagonal (EBD) structures achieve good clustering results. More importantly, LSR produces closed solutions are easier to solve. However, with properties have solved using sensit...