Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance
Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance
In a linear model, consider the class of estimators that are equivariant with respect to linear transformations of the predictor basis. Each of these estimators determines an equivariant linear prediction rule. Equivariant prediction rules may be appropriate in settings where sparsity assumptions (like those common in high-dimensional data analysis) are …