Compute the magnitude (i.e., the Euclidean norm) of each column in the \(\mathbf{Z}\) matrix. Select the column with the largest magnitude as the most estimable parameter. Set \(k\ \ =\ 1\). Construct the matrix \(\mathbf{X}_{k}\) by including the \(k\) selected columns from \(\mathbf{Z}\) that correspond to parameters that have been ranked. Use \(\mathbf{X}_{k}\) to predict columns in \(\mathbf{Z}\) using ordinary least squares: \({\hat{\mathbf{Z}}}_{k}=\left(\mathbf{X}_{k}^{T}\mathbf{X}_{k}\right)^{-1}\mathbf{X}_{k}^{T}\mathbf{Z}\) (2.1)