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We review variable selection and variable screening in high-dimensional linear models. Thereby, a major focus is an empirical comparison of various estimation methods with respect to true and false positive selection rates based on 128 different sparse scenarios from semi-real data (real data...
Many present day applications of statistical learning involve large numbers of predictor variables. Often, that number is much larger than the number of cases or observations available for training the learning algorithm. In such situations, traditional methods fail. Recently, new techniques...
This paper consider penalized empirical loss minimization of convex loss functions with unknown non-linear target functions. Using the elastic net penalty we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target...
principle components and other shrinkage techniques, including Bayesian model averaging and various bagging, boosting, least …
popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information … criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this …
Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling...
We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top …
We apply the Diebold-Yilmaz connectedness index methodology on sovereign credit default swaps (SCDSs) to estimate the network structure of global sovereign credit risk. In particular, using the elastic net estimation method, we separately estimate networks of daily SCDS returns and volatilities...