Real data often contain anomalous cases, also known as outliers. These may spoil the
resulting analysis but they may also contain valuable information. In either case, the ability to …

When analyzing data, outlying observations cause problems because they may strongly
influence the result. Robust statistics aims at detecting the outliers by searching for the …

A collection of robust Mahalanobis distances for multivariate outlier detection is proposed,
based on the notion of shrinkage. Robust intensity and scaling factors are optimally …

The scores of distance-based outlier detection methods are difficult to interpret, and it is
challenging to determine a suitable cut-off threshold between normal and outlier data points …

Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

A renewed interest in data analytics and decision support systems in develo** automated
computer systems is facilitating the emergence of hybrid intelligent systems by combining …

The autocorrelation function (acf) and the partial autocorrelation function (pacf) are
elementary tools of linear time series analysis. The sensitivity of the conventional sample acf …

Current challenges triggered by the limited interactions between people and institutions
during the pandemic crisis have emphasized the need to strengthen the digitization process …

The sample skewness and kurtosis of macroeconomic and financial time series are routinely
scrutinized in the early stages of model-building and are often the central topic of studies in …

Real-world datasets are often characterised by outliers, points far from the majority of the
points, which might negatively influence modelling of the data. In data analysis it is hence …