Spearman's rank correlation is a robust alternative for the standard correlation coefficient. By using ranks instead of the actual values of the observations, the impact of outliers remains limited. In this paper, we study an estimator based on this rank correlation measure for estimating...

Sparse partial robust M regression is introduced as a new regression method. It is the first dimension reduction and regression algorithm that yields estimates with a partial least squares alike interpretability that are sparse and robust with respect to both vertical outliers and leverage...

This paper compares Bayesian estimators with different prior choices for the time variation of the coefficients of Time Varying Parameter Vector Autoregression models using Monte Carlo Simulations. Since the commonly used prior choice only allows for a tiny amount of time variation, less...

We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector - their main credit providers. The use...

In recent years, the market share of socially responsible investment funds has rapidly increased. This has sparked interest of academics and practitioners for the impact of single stock sustainability screening on portfolio performance. Besides eliminating non sustainable assets from the...

Linear discriminant analysis for multiple groups is typically carried out using Fisher's method. This method relies on the sample averages and covariance matrices computed from the different groups constituting the training sample. Since sample averages and covariance matrices are not robust, it...

The ongoing unification which takes place on the European political scene, along with recentadvances in consumer mobility and communication technology, raises the question whether theEuropean Union can be treated as a single market to fully exploit the potential synergy effectsfrom pan-European...

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set, who might affect the estimated model and the associated classification error rate. In this paper, the effect of observations in the training...

Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves...

This paper compares methods for variability extraction from a univariate time series in real time. The online scale estimation is achieved by applying a robust scale functional to a moving time window. Scale estimators based on the residuals of a preceding regression step are compared with...