Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, however, been formalized in the sixties by the pioneering work of Huber and Hampel. Robust statistics is an extension of classical statistics, which takes into account the fact that models assumed to...

Data revisions in macroeconomic time series are typically studied in isolation ignoring the joint behaviour of revisions across different series. This ignores (i) the possibility that early releases of some series may help forecast revisions in other series and (ii) the problems statistical...

Many multivariate methods that are apparently distinct can be linked by introducing one or more parameters in their definition. Methods that can be linked in this way are correspondence analysis, unweighted or weighted logratio analysis (the latter also known as quot;spectral mappingquot;),...

Missing data is a problem appearing ubiquitously across many fields and needs to be dealt with systematically. For multivariate time series data imputation can be a challenging problem. We consider the particular case of credit default swap time series, where missing data can pose a considerable...

This manuscript is the author's response to: "Dochtermann, N.A. & Jenkins, S.H. Multivariate methods and small sample sizes, Ethology, 117, 95-101." and accompanies this paper: "Budaev, S. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines....

In this paper we show how to apply Bayesian methods to noisy ratio scale distances for both the classical similarities problem as well as the unfolding problem. Bayesian methods produce essentially the same point estimates as the classical methods but are superior in that they provide more...