Shrinking in COMFORT

The present paper combines nonlinear shrinkage with the Multivariate Generalized Hyperbolic (MGHyp) distribution to account for heavy tails in estimating the first and second moments in high dimensions. An Expectation-Maximization (EM) algorithm is developed that is fast, stable, and applicable in high dimensions. Theoretical arguments for the monotonicity of the proposed algorithm are provided and it is shown in simulations that it is able to accurately retrieve parameter estimates. Finally, in an extensive Markowitz portfolio optimization analysis, the approach is compared to state-of-the-art benchmark models. The proposed model excels with a strong out-of-sample portfolio performance combined with a comparably low turnover

Unauthorized fictions: political conflict as spectacle and the question of trust in the age of Trump

Why do supporters of former US president Donald Trump make short tribute videos which resemble mainstream action film trailers with their idol as the protagonist? And why does the Trump campaign use a similar trailer template for video of rallies and campaign spots? This contribution traces the increasing use of cinematic storytelling templates in the digital media environment, particularly for Trump’s right-wing politics. We focus on tribute and campaign videos which appeal to the viewer’s tacit knowledge of the trailer format to make political conflicts legible as dramatic confrontations. We argue that their stylization of political conflict as spectacle should be understood as an example of “ocular democracy” (Green 2011), in which the gaze, rather than the voice, is the source of popular empowerment. To the extent that these films signal a threat to liberal democracy, it lies not in the narrativization of conflict in cinematic terms, but in the propagation of generalized distrust in combination with particularized trust in the figure of the demagogue

On the Use of Random Forest for Two-Sample Testing

We follow the line of using classifiers for two-sample testing and propose tests based on the Random Forest classifier. The developed tests are easy to use, require almost no tuning and are applicable for any distribution on $\mathbb{R}^d$. Further, the built-in variable importance measure of the Random Forest gives potential insights which variables make out the difference in distribution. We add to the theoretical treatment for the use of classification for two-sample testing. Finally, two real world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, we also provide the R-package ``hypoRF''

R-NL: Fast and Robust Covariance Estimation for Elliptical Distributions in High Dimensions

We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estimator in a wide range of simulation scenarios. Finally, an empirical application demonstrates its state-of-the-art performance on real data

On the use of random forest for two-sample testing

Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on R^d. Furthermore, the built-in variable importance measure of the Random Forest gives potential insights into which variables make out the difference in distribution. An asymptotic power analysis for the proposed tests is conducted. Finally, two real-world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, the R-package “hypoRF” is provided

R-NL: Covariance Matrix Estimation for Elliptical Distributions based on Nonlinear Shrinkage

We combine Tyler's robust estimator of the dispersion matrix with nonlinear shrinkage. This approach delivers a simple and fast estimator of the dispersion matrix in elliptical models that is robust against both heavy tails and high dimensions. We prove convergence of the iterative part of our algorithm and demonstrate the favorable performance of the estimator in a wide range of simulation scenarios. Finally, an empirical application demonstrates its state-of-the-art performance on real data

Heterogeneous Tail Generalized Common Factor Modeling

A multivariate normal mean-variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a rich dependency structure for capturing the stylized facts of the data. It generalizes several existing model structures, with or without financial factors. It is further applicable in large dimensions due to a fast ECME estimation algorithm of all the model parameters. The advantages of modelling financial factors and asset returns jointly under non-Gaussian errors are illustrated in an empirical comparison study between the proposed Factor-HGH model and classical financial factor models. While the results for the Fama-French 49 industry portfolios are in line with Gaussian-based models, in the case of highly tail heterogeneous cryptocurrencies, the portfolio based on the Factor HGH model doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected-shortfall at a low level

Reduction of Birth Weight Among Infants Born to Adolescents: Maternal–Fetal Growth Competition

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72838/1/j.1749-6632.1997.tb48213.x.pd

Appropriate age range for introduction of complementary feeding into an infant’s diet

Peer reviewedPublisher PD