estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it … is standard in decision theory and in empirical Bayesian statistics. Our shrinkage estimator can be seen as a way to …

component analysis (SPCA), coupled with a variety of other factor estimation as well as data shrinkage methods, including … nonlinear methods, and that using a combination of factor and other shrinkage methods often yields superior predictions. For … forms of shrinkage. For example, SPCA yields MSFE-best prediction models in many cases, particularly when coupled with …

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal …-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered … – 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results …

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal …-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered … -- 2010 I exhaustively evaluate the forecasting properties of Bayesian shrinkage in regressions with many predictors. Results …

, which is generally the scale of interest to the analyst.We then show that with nonlinear models shrinkage due to overfitting … gets confounded by shrinkage—or expansion— arising from model misspecification. Out-of-sample predictive calibration can in …